CHAPTER-II Control Charts for Fraction Nonconforming using m-of-m Runs Rules
|
|
- Cecil Marshall
- 6 years ago
- Views:
Transcription
1 CHAPTER-II Control Charts for Fraction Nonconforming using m-of-m Runs Rules. Introduction: The is widely used in industry to monitor the number of fraction nonconforming units. A nonconforming unit is a roduct which fails to meet at least one secified requirement. The control limits and erformance study of the fraction nonconforming control are tyically based on the binomial distribution. The samle fraction nonconforming is defined as the ratio of the number of nonconforming units (X) in the samle to the samle size (n). That is X ˆ. (.) n It is clear that X follows binomial distribution with arameters n and where is the robability that an unit is nonconforming. The objective of a control is to control the quality of the characteristic or to detect quickly any increase in a rocess fraction nonconforming ( ). When the rocess is in the state of in-control the mean and variance of ˆ are and resectively where is the fraction nonconforming in the n roduction rocess when the rocess is in the in-control state. If is unknown it will be estimated from observed data. The control limits for using normal aroximation are given by UCL CL LCL ( ( ) / n ) / n.
2 Alternatively the could be based on standardized statistic Z where Z is defined as follows ˆ Z (.) ( )/ n Here Z is aroximately distributed as a standard normal variate. The Shewhart X and control s are most oular control s resectively for monitoring mean and fraction nonconforming of a rocess distribution. The Shewhart standard control s are based on the three sigma control limits and give out-of-control signal if a single oint lots outside the control limits. To detect large shifts in a rocess the Shewhart control s are sensitive; however they are insensitive for small shifts. To enhance the effectiveness of the Shewhart control s to detect small shifts various runs rule are suggested and studied by several authors in literature. In order to study erformance of any one of the oularly used measures is the average run length (ARL) of a control. Boorke and Evans (97) have develoed the arkov chain aroach to determine the average run length moments and ercentage of oints of the run length distribution. Cham and Woodall (987) emloyed arkov chain aroach to derive ARL for the Shewhart with sulementary runs rules. In literature m-of-k have been studied by various researchers. The m-of-k signals if consecutive m oints of k oints lie outside control limit. Hurwitz and athur (99) suggested to combine -of- rule with the -of- runs rule and to use.5 warning limits on the X. Klein () suggested the two alternatives to the Shewhart X and develoed the -of- and -of- control s which have symmetric uer and lower control limits. Both s have a better erformance than the Shewhart X. Khoo () studied the erformance of -of- -of- -of-4 -of- and -of-4 control s. He suggested the -of- and -of-4 control s for detecting 4
3 the small to moderate shifts. Khoo and Ariffin (6) roosed two imroved runs rules to enhance the erformance of the two rules suggested by Klein () for the detection of the large shifts. Acosta-ijia (7) studied the statistical characteristics of both m-of-m runs rule and (m-)-of-m runs rule to sulement the Shewhart control. Zang and Wu (5) studied the design of control with sulementary runs rules. Antzoulakos and Raktitzis (8) suggested the modified r-of-m control to detect the small to moderate shifts in the rocess mean. Acosta-ijia and Pignatiello (9) studied the erformance of k-of-k runs rules for monitoring the rocess standard deviation. Lim and Cho (9) investigated the economic-statistical roerties of X sulemented with m-of-m runs rules and also studied the steady-state ARL. To monitor fraction nonconforming of a manufacturing rocess Khoo () roosed the -of- -of- and -of-4 control s are as an alternative to the Shewhart standard. All roosed control s are easy to imlement and have better erformance than the standard. Literature review reveals that -of- for fraction nonconforming have not been discussed so far to the best of our knowledge. Also steady-state erformance of m-of-m runs rules are not studied in resence or absence of warning limits. In this Chater we roose control s for fraction nonconforming using m-of-m runs rules with and without warning limits to study the zero-state and steady-state roerties. The zero-state mode in which rocess shifts can occur at the beginning of the samling interval. Rest of the Chater is organized as in Section. we describe m-of-m runs rules without warning limits and reort the erformance of the m-of-m studied under zero-state and steady-state mode. While Section. gives m-ofm with warning limits and its erformance is studied under only steadystate mode. In Section.4 we briefly describe cumulative count of conforming the m-of-m cumulative count of conforming control 5
4 and its erformance under steady-state mode is given. Section.5 gives conclusions.. Runs Rules Schemes without Warning Limits: Consider a control with two control limits (UCL LCL). Let us consider the three regions for a control. The region between uer control limit and lower control limit (Region ). The region above uer control limit (Region ). The region below lower control limit (Region ). These three regions are shown in the Figure.. Region UCL Region CL Region LCL Figure.: Fraction nonconforming control. The robability of single oint falls in the regions are denoted by c u l resectively. When a fraction nonconforming is then c rlcl Z UCL where we assume that the control statistic ˆ Z to follow a n / standard normal distribution. 6
5 Now c r LCL / n ˆ UCL / n LCL r ( )/ n UCL ( ) / n Z ( )/ n ( )/ n UCL / / n LCL n / n / n. Similarlly u UCL LCL l / / / n n / n n where (.) denotes the distribution function of a standard normal distribution... The m-of-m : We consider the information from multile samles including the most recent one to make a decision to signal an out-of-control status. This is achieved by considering a run of the samle oints that fall beyond the control limits including the current samle and one or more samles from the recent ast samles. The m-of-m signals an out-of-control status at any time oint when m-consecutive samle oints fall either above UCL or below LCL. Suose mm denotes the event when two successive oints fall in region m. The control signals an out-of-control status when an event D... occurs. Define states of the arkov chain as follows: mtimes mtimes State : One oint fall between both control limits. State : One oint falls above uer control limit. State : One oint falls below lower control limit. 7
6 State 4: Two consecutive oints fall above uer control limit. State 5: Two consecutive oints fall below lower control limit. State 6: Three consecutive oints fall above uer control limit. State 7: Three consecutive ints fall below lower control limit and so on. Finally State m: Out-of-control (absorbing) state with associated attern given by the set D. The arkov chain reresentation of consist of m states with the first ( m ) of them being transient. A state is said to be transient state if and only if starting from state one the robability of returning to state one after some finite length of time is less than one. Then the matrix can be artitioned as m m transition robability Q P ( I Q) J where Q is the ( m ) (m ) transition robability matrix for the transient sates I is the ( m ) (m ) identity matrix and J is the column vector of one of an order ( m ). The robability mass function (.m.f.) the exected value and the variance of the run length random variable T are resectively given by and Var r T i eq I Q J i... i (.) ei Q J (.4) E T T ei QI Q J ET (.5) 8
7 where... e is the initial distribution. Let j be the exected m value of the waiting time from state j until the first occurrence of D. Thus if rocess is initially in-control is the ARL. Let... m be the vector of average run lengths. By taking exectations conditional uon the result of the first subgrou these exected values can be found by solving the following linear system of equations corresonding to I QJ where is the column vector of ones (see for examle Acosta-ejia and Pignatiello (9)). 4 5 c. c. c. c. c.... m4 m m m c. c. c. c. u. u. u. u. u. l. u. l. u. l. l. l. l. l.... u l. m By solving the above linear system of equations the ARL for a with m-of-m runs rule m is given by m m m u l m m m m u l u. l u l c u l. (.6) In the next subsections we describe -of- -of- and -of- s as an illustration of m-of-m. 9
8 .. The -of- : The -of- (standard ) signals an out-of-control status if a oint falls either above uer control limit or below a lower control limit. The -of- is Shewhart control... The -of- : The -of- for a fraction nonconforming is studied by Khoo (). The -of- signals an out-of-control status if two consecutive oints ( Z i and Z i ) fall either above an uer control limit or below lower control limit. In other words if two successive oints fall in the region or region the -of- signals an out-of-control status...4 The -of- : In literature the develoment of -of- for a fraction nonconforming has not been reorted to the best of our knowledge. Therefore we study the -of- control to increase the sensitivity of control for fraction nonconforming. The -of- signals an out-of-control status if three consecutive oints ( Z i Zi and Z i ) fall either above uer control limit or below lower control limit. We now resent the arkov chain aroach to obtain ARL of with - of- runs rule. Consider an absorbing arkov chain with five transient states which are defined as follows. State : A oint falls between both control limits. State : A oint falls above an uer control limit. State : A oint falls below lower control limit. State 4: Two successive oints fall above uer control limit. State 5: Two successive oints fall below lower control limit.
9 State 6: An absorbing state three successive oints falls either above uer control limit or below lower control limit. Then the 6 6 one ste transition robability matrix of the arkov chain can be exressed as follows. c c c c c u u u l l l u l. u l (.7) Let Q be matrix obtained from the matrix defined in (.7) by removing the last row and column. ARL of -of- is obtained by solving the linear system corresonding to QJ vector of ones I where I is 5 5 identity matrix and J is column 4 5 c. c. c. c. c. u. l. u. l. u. l. u. l 5 4 (.8) where j denotes the exected value of the waiting time from state j to until the first occurrence of an out-of-control signal. Let 4 be the vector of ARL s. Solving the above linear system of equations the ARL of -of- is given by u l u l u. l u l c u l 5. (.9) Let u u l and c u l hence c u. Substituting c u in equation (.9) gives an in-control average run length (ARL())
10 u u ARL (). (.) u ( u) ( u)( u u ) Now if we set ARL( ) 7.4 then from equation (.) u. 56 and using standard normal distribution the control limits are.7. In the following subsection we gives erformance of m-of-m...5 Performance Study of the m-of-m Control Chart: The ARL erformance of based on the standardized statistic Z defined in equation (.) with control limits (-of- ) the -of- and -of- control s are given in Table.. We assume. 5for this study. Similarly the comuted ARL values based on. are given in Table. for rules -of- -of- and -of-. The ARL is comuted under zero-state mode. Since the ARL as a single arameter is not necessarily very tyical value of the run length distribution. Therefore the standard deviation of the run length (SDRL) distribution is also given in arentheses. The -of- and - of- control s are simle and easy to imlementation for ractitioners; we consider only these two control s.
11 Table.: ARL and SDRL rofile of m-of-m for n= ARL( ) 7.4 and of- 7.4 (69.9).4 (.74) 94.8 (94.) 44.8 (4.88).9 (.4).4 (.5) 8.9 (7.57) 5.4 (4.87).85 (.).9 (.5). (.7).9 (.).6 (.).44 (.8). (.64). (.5) -of- 7.7 (68.9) 88.6 (87.8) 7.5 (7.9).64 (.7) 7. (5.66).6 (8.84) 6.78 (5.47) 4.96 (.64).89 (.56). (.87).8 (.4).54 (.8).5 (.84). (.66).5 (.5).9 (.4) -of- 7.9 (68.) (76.67) (64.55) 9.94 (7.78) 6. (4.).9 (8.) 7. (5.5) 5.58 (.5) 4.6 (.5) 4.4 (.84).66 (.4).4 (.7).6 (.8).6 (.64). (.49).6 (.8) In above table values in arenthesis indicate SDRL.
12 Table.: ARL and SDRL rofile of m-of-m for n= ARL( ) 7.4 and of- 7.4 (69.9) 67.7 (66.77) 6.6 (6.) 6.75 (6.5).5 (.84) 7.6 (7.9) 4.84 (4.).6 (.8).5 (.96). (.4).67 (.6).45 (.8). (.64). (.5).4 (.4).9 (.).6 (.5).4 (.) -of- 7.7 (68.9) 48.5 (47.9) (48.9).5 (9.9).6 (9.74) 6.86 (5.55) 4.8 (.5).7 (.7).8 (.69).68 (.6).44 (.95).8 (.7).7 (.57). (.4).7 (.4).4 (.6). (.9). (.4) -of- 7.9 (68.) 4.8 (9.5) (44.6).4 (8.8).4 (9.7) 7.48 (5.4) 5.58 (.5) 4.55 (.4).95 (.75).59 (.9).6 (.98). (.74). (.57).8 (.4).5 (.). (.5). (.8). (.) In above table values in arenthesis indicate SDRL. 4
13 The results in Tables. and. show the erformance of -of- is better than the -of- and -of- s. On the contrary -of- outerforms for larger shifts. This is only minor concern since for small shifts -of- and -of- s erforms significantly better than the -of-. It is also observed that standard deviation of the run length distribution is significantly large. Such a henomenon is also observed by Antzoulakos and Rakitzis (8). The run length distribution of m-of-m control is a highly skewed distribution with a right tail which decreases slowly for small shifts in a rocess fraction nonconforming. In such cases quantiles give more information about erformance study of a control (see for examle Shmueli and Cohen ()). So we also study three quartiles denoted by Q Q and Q of the run length distribution of roosed m-of-m control. Quartiles of the run length distribution are obtained using equation (.5) under zero-state mode. Table. and Table.4 give the three quartiles and inter quartile ranges (IQR) of the run length distribution associated with m-of-m control when ARL()= 7.4. Table.: Quartiles and IQR of m-of-m control under zero-state mode when. 5and n=. -of- -of- -of- Q Q Q IQR Q Q Q IQR Q Q Q IQR
14 Table.4: Quartiles and IQR of m-of-m control under zero-state mode when. and n=. -of- -of- -of- Q Q Q IQR Q Q Q IQR Q Q Q IQR The results in Tables. and.4 show that the quartiles and IQR values of the -of- are smaller than the -of- as well as -of- s. The -of- has a significantly better erformance than the -of- and -of- to detect small to moderate shifts. We describe steady-state ARL of m-of-m in the next subsection...6 Steady-State Average Run Length: Crosier (986) suggested a technique for obtaining steady-state ARL. After that many researchers reorted the study of steady-state roerties of a control in literature. Here we study the steady-state roerties of m-of-m fraction nonconforming control. If rocess is running for a long eriod in an in-control condition it will reach in a steady-state condition. In order to study the long term roerties of a control it is aroriate to investigate the steady-state average run length (SSARL). 6
15 Let Q be a square matrix obtained from Q by imosing the condition that no T signal occurs. Let... m be the vector of steady-state robabilities for the in-control transient states. The steady-state robabilities can be obtained by solving the following equations: T T T Q and. (.) m Under the in-control situation let c and u c l. As an illustration the conditional transition robability matrix for in-control states for m= is Q C C C C C C C C C C C C C where C u C u C u and u C u. u The stationary robabilities from equation (.) are given by C C C C C 4 5. C C C C C The SSARL can be obtained by where ARL T SSARL ARL (.) I Q J T... m 7
16 The in-control SSARL can be obtained as T SSARL( ) ARL(). (.) For -of- the ARL vector can be obtained by solving linear system of equations given in equation (.8) as l l u u D l l u D u u l D 5 4 l l D u u D where u l c u u l D u. l l. The SSARL of -of- can be obtained by SSARL. (.4) D The in-control SSARL of -of- can be obtained by substituting u u l and c u l in equation (.4) as u u.. u u u. u u u u. u u u SSARL ( ). (.5) 8
17 Suose that the desired in-control SSARL is aroximately 7.4. Solving equation (.5) for u gives u The control limits for -of- control are. using standard normal distribution. In the following subsection we study the steady-state erformance of m-of-m...7 Steady-State Performance of the m-of-m Control : The SSARL erformance of based on the standardized statistic Z sulemented with runs rules -of- and -of- for. 5and n= are given in Table.5. Table.6 gives the SSARL of -of- -of- and -of- control s for. and n=. The SDRL is also given in arentheses. We observe that from Tables.5 and.6 the erformance of -of- is significantly better than the -of- as well as -of- control s to detect small to moderate shifts. The -of- has a higher ower of detecting outof-control signal. 9
18 Table.5: SSARL and SDRL rofile of m-of-m for n= ARL( ) 7.4 and.5. -of- -of- -of (69.9) 7.6 (69.86) 7.7 (66.4).6.4 (.74) (87.6) (74.54) (94.) 7. (7.) 66.7 (6.) (4.88).49 (.) 9.6 (6.9).9.9 (.4) 6.89 (5.69) 5.9 (.49)..4 (.5).7 (8.85) 9.97 (7.66). 8.9 (7.57) 6.7 (5.49) 7. (4.76). 5.4 (4.88) 4.89 (.65) 5.4 (.4)..85 (.).84 (.57) 4.47 (.5).4.9 (.5).9 (.88).89 (.49).5. (.7).77 (.4).5 (.) In above table values in arenthesis indicate SDRL.
19 Table.6: SSARL and SDRL rofile of m-of-m for n= ARL( ) 7.4 and.. -of- -of- -of (69.9) 7.6 (69.86) 7.7 (66.4) (66.76) 48. (47.4) 4.9 (7.59). 6.6 (6.) 49.5 (48.8) 46.7 (4.5) (6.5).9 (9.7). (7.59).4.5 (.84).97 (9.76). (8.77) (7.) 6.79 (5.56) 7.9 (5.) (4.) 4.76 (.5) 5.4 (.5).7.6 (.8).66 (.9) 4.4 (.8).8.5 (.95). (.7).8 (.9).9. (.4).64 (.7).45 (.88)..67 (.6).4 (.97). (.4) In above table values in arenthesis indicate SDRL.
20 In Table.7 we resent control limits of the -of- -of- -of- control s under zero-state and steady-state mode. The control limits of -of- are same under zero-state and steady-state mode. However there is no significant change in the control limits of the m-of-m with m= under zero-state and steady-state mode. Therefore there is no significant difference observed between zero-state ARL and SSARL. Table.7: Control limits for m-of-m. Control limits -of- -of- -of- Zero-state Steady-state. 78. In the next section we study steady-state roerties of m-of-m with warning limits. To imrove the overall erformance of s we design s with combined -of- and m-of-m runs rules.. The m-of-m Runs Rules Schemes with Warning Limits: The case of control s with warning limits was first extensively studied by Page (955) who roosed four runs rules. He also studied the erformance of control s by introducing a arkov chain aroach to calculate exact run length distribution. osteller (94) Dudding and Jannett (94) and Weiler (95) have discussed the case of warning limits and runs rules. An extensive review on warning limits and runs rules can be found in Koutras al et. (7). Warning limits should be drawn in less extreme osition than the control limits. If secified number of oints falls between warning limit and control limit control signals an out-of-control status. To increase sensitivity of with sulementary runs rules we roose m-of-m with warning limits. For this we first define following notations: UWL- denote uer warning limit.
21 LWL - denote lower warning limit. - the robability of a single oint falling above UCL. - the robability of a single oint falling below LCL. c - the robability of a single oint falling between both warning limits. u - the robability of a single oint falling between uer control limit and uer warning limit. l - the robability of a single oint falling between lower control limit and lower warning limit. Consider a control based on standardized statistic Z with two control limits (UCL= k LCL= -k) and two warning limits (UWL= w LWL= -w). Let us consider five regions for the control : The region between both warning limits (Region ). The region between uer control limit and uer warning limit (Region ). The region between lower control limit and lower warning limit (Region ). The region above uer control limit (Region 4). The region below lower control limit (Region 5).
22 4 These five regions are shown in the following Figure.. Figure.: Fraction non-confirming control with warning limits. We assign numbers 4 and 5 for above defined regions 4 5 resectively. Thus a set 45 A denotes ossible numbers assigned to regions. The robability that a single oint falls in regions 4 and 5 are resectively given by l u c and where / / / / n n LWL n n UWL c / / / / n n UWL n n UCL u / / / / n n LCL n n LWL l / / n n UCL Region 5 LCL Region LWL Region CL UWL UCL Region 4 Region
23 and LCL / n / n Under we have c and u u l. The rocess is in the in-control state if and in the out-of-control state otherwise. The m-of-m fraction nonconforming control signals an out-of-control status if a oint falls above (below) uer (lower) control limit or m- consecutive oints fall between UWL and UCL or LCL and LWL. In other words the m-of-m signals if an event mtimes mtimes occurs. The states of the arkov chain are defined as follows: State : One oint falls between warning limits. State : One oint between UWL and UCL. State : One oint between LCL and LWL. State 4: Two consecutive oints between UWL and UCL. State 5: Two consecutive oints between LCL and LWL. State 6: Three consecutive oints between UWL and UCL. State 7: Three consecutive ints between LCL and LWL and so on. Finally State m: Out-of-control (absorbing) state with associated attern given by the set. 5
24 Further rocedure of obtaining SSARL of m-of-m is same as given in subsection..6. To find the warning limits of control under steady-state mode suose that the desired SSARL() is aroximately 7.4. We may note that the and ARL() are functions of m control limit (k) and warning limit (w) only. Therefore for a given combination of m and control limit (k) the warning limits can be determined from equation (.). In Section. we studied zero-state and steady-state roerties of m-of-m without warning limits. We observed that if the rocess running in an incontrol state for long time eriod or rocess shift is occurs at any time in the samling interval the erformance of m-of-m is aroximately same in both the situations. Therefore in this section we study only steady-state roerties of the m-of-m... Steady-State erformance Study of m-of-m Control Chart with Warning Limits: We investigate SSARL roerties of -of- and -of- control s and comare its erformance with the standard (-of- ). We choose k=. and. to comare s under study. The SDRL is also given in arentheses along with the SSARL since SSARL alone does not reveal imortant information regarding the erformance of a control. The steady-state SDRL can be obtained using equation (.5). Table.8 gives the results of the SSARL and SDRL of the Shewhart standard (-of- ) - of- and -of- control s. 6
25 Table.8: SSARL and SDRL of m-of-m control with. 5 n= of- 7.4 (69.9) 94.4 (9.84) 6.8 (5.78).8 (9.87) 5. (4.8).9 (.74). (.75).8 (.).5 (.87). (.64). (.49) k k. -of- w.899 -of- w.47 -of- w.876 -of- w (69.8) 94.8 (9.78) 6.4 (5.74).7 (9.85) 5. (4.79).8 (.7). (.74).79 (.9).5 (.86). (.64). (.49) (69.85) 94.4 (9.7) 6.7 (5.65). (9.77) 5.8 (4.7).6 (.69). (.7).79 (.8).49 (.85). (.64). (.49) (69.9) (76.7) 9.8 (8.9) 8.4 (7.4) 4.4 (.54).9 (.9).8 (.8).77 (.99).5 (.75).5 (.59). (.48) (67.4) (7.4) 9.7 (7.44) 8.9 (6.56) 4.58 (.6). (.9).4 (.).88 (.96).59 (.75).9 (.6).6 (.48) In above table values in arenthesis indicates SDRL. 7
26 For the value of control limit k=. the SSARL and SDRL of -of- -of- -of- s are not differ much. However for k=. the SSARL and SDRL values of m-of-m with m= are significantly different. Therefore the value of control k=. is chosen to study the steady-state roerties of m- of-m. The -of- control erforms better than the -of- as well as -of- control s to detect small to moderate shifts in a rocess when k=.. Table.9 and Table. also give the SSARL and SDRL m-of-m with in-control fraction nonconforming.. 5. Table.9: SSARL and SDRL rofile of m-of-m control with. n= k=. k=. -of- -of- -of- -of- -of- w.899 w.47 w.876 w (69.9) (69.8) (69.85) (69.9) (67.4) (66.77) (66.7) (66.66) (4.85) (.8) (6.) (6.5) (6.97) (45.5) (4.5) (6.5) (6.) (6.) (7.95) (5.8) (.84) (.8) (.7) (8.6) (7.59) ( 5.74 (7.9) (7.8) (6.98) 4.8) (4.) (4.) (4.) (4.) (.98) (.6) (.8) (.8) (.75) (.) (.79) (.96) (.95) (.9) (.4) (.) (.4) (.4) (.8) (.8) (.) (.6) (.5) (.4) (.85) (.84) In above table values in arenthesis indicate SDRL. 8
27 Table.: SSARL and SDRL rofile of m-of-m control with.5 n= of- 7.4 (69.9).4 (.74) 94.8 (94.) 44.8 (4.88).9 (.4).4 (.5) 8.9 (7.57) 5.4 (4.87).85 (.).9 (.5). (.7).9 (.).6 (.).44 (.8). (.5) -of- w (69.8).7 (.67) (94.6) 44. (4.8).88 (.7). (.5) 8.7 (7.55) 5.9 (4.86).84 (.).9 (.4). (.7).9 (.).6 (.).44 (.79). (.6) k=. -of- w (69.85).6 (.65) 94.7 (94.8) 44.4 (4.7).78 (.5).9 (.8) 7.99 (7.44) 5. (4.77).8 (.).87 (.9).8 (.68).89 (.7).6 (.99).44 (.78). (.6) -of- w (69.9) (8.9) 7.89 (69.98).7 (9.74) 5.54 (4.57) 9. (8.4) 5.8 (4.89) 4. (.). (.).5 (.6). (.4).8 (.98).6 (.79).46 (.66).5 (.48) k=. -of- w (67.4) (7.48) 64.4 (6.8) 7.7 (5.6) 4. (.45) 8.57 (6.87) 5.76 (4.8) 4. (.75).9 (.9).69 (.44).7 (.).97 (.9).7 (.79).55 (.68).9 (.5) In above table values in arenthesis indicate SDRL. 9
28 From Table.9 and Table. we observed that the SSARL and SDRL values of the -of- control are significantly better than the -of- and - of- control s when k=.. In general the -of- control has a higher ower of detecting an out-of-control signal. Table. and Table. give the three quartiles and IQR of the run length distribution associated with m-of-m control when SSARL()= 7.4. Table.: Quartiles and IQR of m-of-m control under steady-state mode when.5 n=. -of- -of- -of- Q Q Q IQR Q Q Q IQR Q Q Q IQR Table.: Quartiles and IQR of m-of-m control under steady-state mode when. n=. -of- -of- -of- Q Q Q IQR Q Q Q IQR Q Q Q IQR
29 We have seen that from Table. and Table. the erformance of -of- is significantly better than the -of- as well as -of- s. For comarison study of m-of-m with warning limits and without warning limits we rovide SSARL and SDRL of m-of-m in Table.. Table.: SSARL and SDRL of m-of-m when n= and SSARL ( ) Control s with warning limits -of- -of- -of (69.9) (69.9) (67.4) (66.77) (4.85) (.8) (6.) (45.5) (4.5) (6.5) (7.95) (5.8) (.84) (8.6) (7.59) (7.9) (4.8) (4.) (4.) (.98) (.6) (.8) (.) (.79) (.96) (.4) (.)..9.5 (.4) (.8) (.) (.6) (.85) (.84) Control s without warning limits -of- -of- -of (69.9) (69.86) (66.4) (66.76) (47.4) (7.59) (6.) (48.8) (4.5) (6.5) (9.7) (7.59) (.84) (9.76) (8.77) (7.) (5.56) (5.) (4.) (.5) (.5) (.8) (.9) (.8).5..8 (.95) (.7) (.9) (.4) (.7) (.88) (.6) (.97) (.4) In above table values in arenthesis indicate SDRL. It is clear that from Table. the m-of-m with warning limits has a better erformance over the m-of-m without warning limits. The SSARL and SDRL values of m-of-m with warning limits for m= are significantly better than the control without warning limits. 4
30 We investigate steady-state roerties of cumulative count of conforming control in the next section..4 Cumulative Count of Conforming Control Chart: The fraction nonconforming of modern manufacturing rocesses are usually very low at arts er million (m) and one seaks of high yield rocesses. A high yield rocess is defined as a rocess with an in-control fraction nonconforming ( ) is at the most. or m or such a smaller number. The Shewhart is widely used to monitor the fraction nonconforming of items. The Shewhart is not suitable for monitoring the high yield rocesses since for a large subgrou size the number of nonconforming units in a subgrou is assumed to be aroximately normal. When the fraction nonconforming of units is very small normal aroximation might be incorrect. Goh (987) showed that use of Shewhart in high yield rocesses results in high false alarm rates and inability to detect rocess imrovements. In order to rovide adequate statistical rocess control technique for high-yield rocesses cumulative count of conforming (CCC) control based on geometric distribution roosed by Goh (987) as an alternative to the. The concet of cumulative count of conforming items first introduced by Calvin (98) and further studied by Xie and Goh (99 ). Chen and Cheng (8) develoed the design of CCC with sulementary runs rules. The roosed CCC with -of- and -of- runs rules have better erformance than the CCC. The CCC gives an out-of-control signal when a oint falls beyond the control limits. Sulementary runs rules are used to enhance the sensitivity of control s. In the following subsection we describe m-of-m CCC and develo arkov chain model to evaluate the steady-state ARL erformance. 4
31 .4. CCC Control Chart: Goh (987) roosed CCC to monitor high yield manufacturing rocesses. In the CCC the number of items are insected until one nonconforming item is observed for monitoring the manufacturing rocesses follows the geometric distribution. Let X be a random variable having a geometric distribution then its robability mass function is given by r x X x x... (.6) In manufacturing rocesses the value of fraction nonconforming of items ( ) is very small. The cumulative distribution function of random variable X is given by F( x) x x x where x is the largest integer less than x. If the accetable risk of false alarm is assumed equal to the control limits of CCC can be obtained aroximately. The m-of-m CCC gives an out-of-control signal when a oint falls outside of the control limits or m-successive oints fall beyond the control limits. For m-of-m CCC we consider uer control limit (UCL) and lower control limit (LCL). In m-of-m CCC we define three regions: The region between uer control limit and lower control limit (region ). The region above uer control limit (region ). The region below lower control limit (region ). Let u denote the robability that a oint falls above uer control limit. 4
32 l denote the robability that a oint falls below a lower control limit. c denote the robability that a oint falls between both the control limits. The robability of a single oint falls in regions and is given by u FUCL l FLCL and c FUCL FLCL. The rocedure of obtaining SSARL of m-of-m CCC is the same as given in subsection..6. As an illustration we exlain -of- CCC control in detail in the following subsection..4. The -of- CCC Control Chart: Chen et al. (9) has studied the -of- CCC and its ARL comuted under zero-state mode. Here we study the steady-state roerties of -of- CCC. The -of- CCC generate an out-of-control signal if - successive oints falls outside of the control limits of -of-. That is - of- CCC signals when the event D D or occurs where D D X i X and D X i X. For m= the transition i robability matrix is given below: i c Q c c u u l u. T Let denote the vector of steady-state robabilities for the incontrol transient states. The steady-state robabilities can be obtained by solving the following equations: T T T Q and. (.7) The matrix Q can be obtained from Q by imosing the condition that there is no signal. The matrix Q is given below: 44
33 Q C C C C C C C From equation (.7) we have C C. C. C C ( C Then SSARL of -of- CCC is given by T SSARL ARL where. ) ARL ( I Q) ARL l. u u l c u l l. u c( l)(. u) Therefore SSARL.... Let u l u and c u l. Now SSARL of -of- becomes SSARL ( u). (.8) u It is easy to solve equation (.8) for given desired SSARL(). Therefore the secific control limits of -of- CCC can be exressed as follows: UCL LCL of of ln( u) ln( ) ln( l). ln( ) The following subsection gives erformance study of m-of-m CCC. 45
34 .4.4 Performance Study of m-of-m CCC Control Chart: In the erformance study we resent the SSARL and SDRL of CCC (- of- CCC ) with sulementary runs rules (-of- and -of- s). For comarison study the SSARL and SDRL of -of- are used as a baseline. Suose that accetable fraction nonconforming equals to.5 and desired SSARL() s are aroximately 7 and resectively. Table.4 and Table.5 show the SSARL and SDRL (given in arentheses) of - of- -of- and -of- control s with SSARL()= 7 and.5 resectively. From the Tables.4-.5 we have seen that the out-of-control SSARL and SDRL values of -of- are significantly better than the -of- as well as -of-. The erformance of -of- is also significantly better than the -of- CCC. In general -of- has a higher ower of detecting out-of-control signal in a rocess. It is imortant to note that when the fraction nonconforming increases out-of-control SSARL and SDRL values of the m-of-m are greater than the in-control SSARL and SDRL. This has been identified by Xie et al. () and studied by several researchers (see for examle Cheng and Chen (8)). For the m-of-m we also comuted three quartiles and IQR of run length distribution. Table.6 and Table.7 show the results of quartiles and IQR of m-of-m. Conclusion from quartiles and IQR is that the erformance of -of- is better than the -of- and -of- s. 46
35 Table.4: SSARL and SDRL rofile of m-of-m when SSARL()=7 and.5. -of- -of- -of (.78) (4.5) (5.8) (.4) (6.7) (.79) (5.46) (55.7) (8.6) (78.87) (7.) (9.5) (69.7) (69.5) (66.77) (44.67) (47.4) (.67) (44.44) (7.8) (4.5) (69.84) (98.9) (54.48) (.) (4.8) (5.54) (97.6) (97.87) (89.44) (7.7) (65.66) (7.) (47.84) (4.96) (58.6) (.4) (6.) (4.8) (98.5) (9.55) (4.97) (74.9) (74.56) (6.6) (48.66) (55.7) (8.89) (8.9) (7.6) (.8) (99.7) (7.47) (8.56) (74.7) (6.95) (5.4) In above table values in arenthesis indicate SDRL. 47
36 Table.5: SSARL and SDRL rofile of m-of-m when SSARL()= and.5. -of- -of- -of (.9) (.79) (4.6) (.) (.67) (4.8) (6.6) (8.48) (4.8) (5.8) (.99) (9.4) (99.6) (99.5) (97.) (5.) (.77) (5.45) (.) (.47) (6.58) (99.9) (6.) (9.) (78.9) (.4) (9.) (6.49) (9.7) (7.4) (45.95) (9.77) (56.9) (.79) (78.8) (46.9) (4.66) (59.4) (.74) (7.) (5.56) (8.7) (94.4) (4.5) (.59) (8.) (.78) (5.48) (64.6) (.74) (.4) (5.45) (6.6) (7.9) (4.6) (.) (4.4) In above table values in arenthesis indicate SDRL. 48
37 Table.6: Quartiles and IQR of m-of-m CCC control under steady-state mode when ARL()= 7 and. 5. -of- -of- -of- Q Q Q IQR Q Q Q IQR Q Q Q IQ R 49
38 Table.7: Quartiles and IQR of m-of-m CCC control under steady-state mode when ARL()= and. 5. -of- -of- -of- Q Q Q IQR Q Q Q IQR Q Q Q IQR
39 .5 Conclusions: In this Chater we have studied the m-of-m with warning limits and without warning limits. Performance of the roosed control without warning limits studied under the zero-state and steady-state mode. Proosed - of- has a better erformance than the -of- and -of- s. Fraction nonconforming m-of-m is designed to detect the small to moderate shifts in the rocess fraction nonconforming. An imrovement of the Shewhart - sulemented with m-of-m runs rules with warning limits is better than the control without warning limits. In this Chater we also investigated steady-state roerties of m-of-m CCC control. The steady-state erformance of roosed is significantly better than the regular CCC control. In the next Chater we develo control s for fraction nonconforming due change in location arameter. 5
A MIXED CONTROL CHART ADAPTED TO THE TRUNCATED LIFE TEST BASED ON THE WEIBULL DISTRIBUTION
O P E R A T I O N S R E S E A R C H A N D D E C I S I O N S No. 27 DOI:.5277/ord73 Nasrullah KHAN Muhammad ASLAM 2 Kyung-Jun KIM 3 Chi-Hyuck JUN 4 A MIXED CONTROL CHART ADAPTED TO THE TRUNCATED LIFE TEST
More informationIntroduction to Probability and Statistics
Introduction to Probability and Statistics Chater 8 Ammar M. Sarhan, asarhan@mathstat.dal.ca Deartment of Mathematics and Statistics, Dalhousie University Fall Semester 28 Chater 8 Tests of Hyotheses Based
More informationMULTIVARIATE STATISTICAL PROCESS OF HOTELLING S T CONTROL CHARTS PROCEDURES WITH INDUSTRIAL APPLICATION
Journal of Statistics: Advances in heory and Alications Volume 8, Number, 07, Pages -44 Available at htt://scientificadvances.co.in DOI: htt://dx.doi.org/0.864/jsata_700868 MULIVARIAE SAISICAL PROCESS
More informationA New Optimization Model for Designing Acceptance Sampling Plan Based on Run Length of Conforming Items
Journal of Industrial and Systems Engineering Vol. 9, No., 67-87 Sring (Aril) 016 A New Otimization Model for Designing Accetance Samling Plan Based on Run Length of Conforming Items Mohammad Saber Fallahnezhad
More informationEvaluating Process Capability Indices for some Quality Characteristics of a Manufacturing Process
Journal of Statistical and Econometric Methods, vol., no.3, 013, 105-114 ISSN: 051-5057 (rint version), 051-5065(online) Scienress Ltd, 013 Evaluating Process aability Indices for some Quality haracteristics
More informationTests for Two Proportions in a Stratified Design (Cochran/Mantel-Haenszel Test)
Chater 225 Tests for Two Proortions in a Stratified Design (Cochran/Mantel-Haenszel Test) Introduction In a stratified design, the subects are selected from two or more strata which are formed from imortant
More informationHotelling s Two- Sample T 2
Chater 600 Hotelling s Two- Samle T Introduction This module calculates ower for the Hotelling s two-grou, T-squared (T) test statistic. Hotelling s T is an extension of the univariate two-samle t-test
More informationAdaptive estimation with change detection for streaming data
Adative estimation with change detection for streaming data A thesis resented for the degree of Doctor of Philosohy of the University of London and the Diloma of Imerial College by Dean Adam Bodenham Deartment
More informationCHAPTER 5 STATISTICAL INFERENCE. 1.0 Hypothesis Testing. 2.0 Decision Errors. 3.0 How a Hypothesis is Tested. 4.0 Test for Goodness of Fit
Chater 5 Statistical Inference 69 CHAPTER 5 STATISTICAL INFERENCE.0 Hyothesis Testing.0 Decision Errors 3.0 How a Hyothesis is Tested 4.0 Test for Goodness of Fit 5.0 Inferences about Two Means It ain't
More informationOn the performance of Shewhart-type synthetic and runs-rules charts combined with an chart
On the performance of Shewhart-type synthetic and runs-rules charts combined with an chart Sandile Charles Shongwe and Marien Alet Graham Department of Statistics University of Pretoria South Africa Abstract
More informationUsing a Computational Intelligence Hybrid Approach to Recognize the Faults of Variance Shifts for a Manufacturing Process
Journal of Industrial and Intelligent Information Vol. 4, No. 2, March 26 Using a Comutational Intelligence Hybrid Aroach to Recognize the Faults of Variance hifts for a Manufacturing Process Yuehjen E.
More informationGuaranteed In-Control Performance for the Shewhart X and X Control Charts
Guaranteed In-Control Performance for the Shewhart X and X Control Charts ROB GOEDHART, MARIT SCHOONHOVEN, and RONALD J. M. M. DOES University of Amsterdam, Plantage Muidergracht, 08 TV Amsterdam, The
More informationLower Confidence Bound for Process-Yield Index S pk with Autocorrelated Process Data
Quality Technology & Quantitative Management Vol. 1, No.,. 51-65, 15 QTQM IAQM 15 Lower onfidence Bound for Process-Yield Index with Autocorrelated Process Data Fu-Kwun Wang * and Yeneneh Tamirat Deartment
More informationEcon 3790: Business and Economics Statistics. Instructor: Yogesh Uppal
Econ 379: Business and Economics Statistics Instructor: Yogesh Ual Email: yual@ysu.edu Chater 9, Part A: Hyothesis Tests Develoing Null and Alternative Hyotheses Tye I and Tye II Errors Poulation Mean:
More informationEcon 3790: Business and Economics Statistics. Instructor: Yogesh Uppal
Econ 379: Business and Economics Statistics Instructor: Yogesh Ual Email: yual@ysu.edu Chater 9, Part A: Hyothesis Tests Develoing Null and Alternative Hyotheses Tye I and Tye II Errors Poulation Mean:
More informationMODELING THE RELIABILITY OF C4ISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL
Technical Sciences and Alied Mathematics MODELING THE RELIABILITY OF CISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL Cezar VASILESCU Regional Deartment of Defense Resources Management
More informationSupplementary Materials for Robust Estimation of the False Discovery Rate
Sulementary Materials for Robust Estimation of the False Discovery Rate Stan Pounds and Cheng Cheng This sulemental contains roofs regarding theoretical roerties of the roosed method (Section S1), rovides
More informationMULTIVARIATE SHEWHART QUALITY CONTROL FOR STANDARD DEVIATION
MULTIVARIATE SHEWHART QUALITY CONTROL FOR STANDARD DEVIATION M. Jabbari Nooghabi, Deartment of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad-Iran. and H. Jabbari
More informationMONITORING BIVARIATE PROCESSES WITH A SYNTHETIC CONTROL CHART BASED ON SAMPLE RANGES
Blumenau-SC, 27 a 3 de Agosto de 217. MONITORING BIVARIATE PROCESSES WITH A SYNTHETIC CONTROL CHART BASED ON SAMPLE RANGES Marcela A. G. Machado São Paulo State University (UNESP) Departamento de Produção,
More informationCombining Logistic Regression with Kriging for Mapping the Risk of Occurrence of Unexploded Ordnance (UXO)
Combining Logistic Regression with Kriging for Maing the Risk of Occurrence of Unexloded Ordnance (UXO) H. Saito (), P. Goovaerts (), S. A. McKenna (2) Environmental and Water Resources Engineering, Deartment
More informationUncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning
TNN-2009-P-1186.R2 1 Uncorrelated Multilinear Princial Comonent Analysis for Unsuervised Multilinear Subsace Learning Haiing Lu, K. N. Plataniotis and A. N. Venetsanooulos The Edward S. Rogers Sr. Deartment
More informationDETC2003/DAC AN EFFICIENT ALGORITHM FOR CONSTRUCTING OPTIMAL DESIGN OF COMPUTER EXPERIMENTS
Proceedings of DETC 03 ASME 003 Design Engineering Technical Conferences and Comuters and Information in Engineering Conference Chicago, Illinois USA, Setember -6, 003 DETC003/DAC-48760 AN EFFICIENT ALGORITHM
More informationMATHEMATICAL MODELLING OF THE WIRELESS COMMUNICATION NETWORK
Comuter Modelling and ew Technologies, 5, Vol.9, o., 3-39 Transort and Telecommunication Institute, Lomonosov, LV-9, Riga, Latvia MATHEMATICAL MODELLIG OF THE WIRELESS COMMUICATIO ETWORK M. KOPEETSK Deartment
More informationarxiv: v1 [physics.data-an] 26 Oct 2012
Constraints on Yield Parameters in Extended Maximum Likelihood Fits Till Moritz Karbach a, Maximilian Schlu b a TU Dortmund, Germany, moritz.karbach@cern.ch b TU Dortmund, Germany, maximilian.schlu@cern.ch
More informationAvailability and Maintainability. Piero Baraldi
Availability and Maintainability 1 Introduction: reliability and availability System tyes Non maintained systems: they cannot be reaired after a failure (a telecommunication satellite, a F1 engine, a vessel
More informationEstimating function analysis for a class of Tweedie regression models
Title Estimating function analysis for a class of Tweedie regression models Author Wagner Hugo Bonat Deartamento de Estatística - DEST, Laboratório de Estatística e Geoinformação - LEG, Universidade Federal
More informationUnderstanding and Using Availability
Understanding and Using Availability Jorge Luis Romeu, Ph.D. ASQ CQE/CRE, & Senior Member Email: romeu@cortland.edu htt://myrofile.cos.com/romeu ASQ/RD Webinar Series Noviembre 5, J. L. Romeu - Consultant
More informationSynthetic and runs-rules charts combined with an chart: Theoretical discussion
Synthetic and runs-rules charts combined with an chart: Theoretical discussion Sandile C. Shongwe and Marien A. Graham Department of Statistics University of Pretoria South Africa sandile.shongwe@up.ac.za
More informationEstimation of the large covariance matrix with two-step monotone missing data
Estimation of the large covariance matrix with two-ste monotone missing data Masashi Hyodo, Nobumichi Shutoh 2, Takashi Seo, and Tatjana Pavlenko 3 Deartment of Mathematical Information Science, Tokyo
More informationA Comparison between Biased and Unbiased Estimators in Ordinary Least Squares Regression
Journal of Modern Alied Statistical Methods Volume Issue Article 7 --03 A Comarison between Biased and Unbiased Estimators in Ordinary Least Squares Regression Ghadban Khalaf King Khalid University, Saudi
More informationGeneral Linear Model Introduction, Classes of Linear models and Estimation
Stat 740 General Linear Model Introduction, Classes of Linear models and Estimation An aim of scientific enquiry: To describe or to discover relationshis among events (variables) in the controlled (laboratory)
More informationOne-way ANOVA Inference for one-way ANOVA
One-way ANOVA Inference for one-way ANOVA IPS Chater 12.1 2009 W.H. Freeman and Comany Objectives (IPS Chater 12.1) Inference for one-way ANOVA Comaring means The two-samle t statistic An overview of ANOVA
More informationJohn Weatherwax. Analysis of Parallel Depth First Search Algorithms
Sulementary Discussions and Solutions to Selected Problems in: Introduction to Parallel Comuting by Viin Kumar, Ananth Grama, Anshul Guta, & George Karyis John Weatherwax Chater 8 Analysis of Parallel
More informationChapter 7: Special Distributions
This chater first resents some imortant distributions, and then develos the largesamle distribution theory which is crucial in estimation and statistical inference Discrete distributions The Bernoulli
More informationPaper C Exact Volume Balance Versus Exact Mass Balance in Compositional Reservoir Simulation
Paer C Exact Volume Balance Versus Exact Mass Balance in Comositional Reservoir Simulation Submitted to Comutational Geosciences, December 2005. Exact Volume Balance Versus Exact Mass Balance in Comositional
More informationSystem Reliability Estimation and Confidence Regions from Subsystem and Full System Tests
009 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June 0-, 009 FrB4. System Reliability Estimation and Confidence Regions from Subsystem and Full System Tests James C. Sall Abstract
More informationRound-off Errors and Computer Arithmetic - (1.2)
Round-off Errors and Comuter Arithmetic - (.). Round-off Errors: Round-off errors is roduced when a calculator or comuter is used to erform real number calculations. That is because the arithmetic erformed
More informationA generalization of Amdahl's law and relative conditions of parallelism
A generalization of Amdahl's law and relative conditions of arallelism Author: Gianluca Argentini, New Technologies and Models, Riello Grou, Legnago (VR), Italy. E-mail: gianluca.argentini@riellogrou.com
More informationAn Improved Calibration Method for a Chopped Pyrgeometer
96 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 17 An Imroved Calibration Method for a Choed Pyrgeometer FRIEDRICH FERGG OtoLab, Ingenieurbüro, Munich, Germany PETER WENDLING Deutsches Forschungszentrum
More informationDistributed Rule-Based Inference in the Presence of Redundant Information
istribution Statement : roved for ublic release; distribution is unlimited. istributed Rule-ased Inference in the Presence of Redundant Information June 8, 004 William J. Farrell III Lockheed Martin dvanced
More informationState Estimation with ARMarkov Models
Deartment of Mechanical and Aerosace Engineering Technical Reort No. 3046, October 1998. Princeton University, Princeton, NJ. State Estimation with ARMarkov Models Ryoung K. Lim 1 Columbia University,
More informationOutline. Markov Chains and Markov Models. Outline. Markov Chains. Markov Chains Definitions Huizhen Yu
and Markov Models Huizhen Yu janey.yu@cs.helsinki.fi Det. Comuter Science, Univ. of Helsinki Some Proerties of Probabilistic Models, Sring, 200 Huizhen Yu (U.H.) and Markov Models Jan. 2 / 32 Huizhen Yu
More informationUncertainty Modeling with Interval Type-2 Fuzzy Logic Systems in Mobile Robotics
Uncertainty Modeling with Interval Tye-2 Fuzzy Logic Systems in Mobile Robotics Ondrej Linda, Student Member, IEEE, Milos Manic, Senior Member, IEEE bstract Interval Tye-2 Fuzzy Logic Systems (IT2 FLSs)
More information4. Score normalization technical details We now discuss the technical details of the score normalization method.
SMT SCORING SYSTEM This document describes the scoring system for the Stanford Math Tournament We begin by giving an overview of the changes to scoring and a non-technical descrition of the scoring rules
More information8 STOCHASTIC PROCESSES
8 STOCHASTIC PROCESSES The word stochastic is derived from the Greek στoχαστικoς, meaning to aim at a target. Stochastic rocesses involve state which changes in a random way. A Markov rocess is a articular
More informationAsymptotic Properties of the Markov Chain Model method of finding Markov chains Generators of..
IOSR Journal of Mathematics (IOSR-JM) e-issn: 78-578, -ISSN: 319-765X. Volume 1, Issue 4 Ver. III (Jul. - Aug.016), PP 53-60 www.iosrournals.org Asymtotic Proerties of the Markov Chain Model method of
More informationSession 5: Review of Classical Astrodynamics
Session 5: Review of Classical Astrodynamics In revious lectures we described in detail the rocess to find the otimal secific imulse for a articular situation. Among the mission requirements that serve
More informationChapter 7 Rational and Irrational Numbers
Chater 7 Rational and Irrational Numbers In this chater we first review the real line model for numbers, as discussed in Chater 2 of seventh grade, by recalling how the integers and then the rational numbers
More informationDeriving Indicator Direct and Cross Variograms from a Normal Scores Variogram Model (bigaus-full) David F. Machuca Mory and Clayton V.
Deriving ndicator Direct and Cross Variograms from a Normal Scores Variogram Model (bigaus-full) David F. Machuca Mory and Clayton V. Deutsch Centre for Comutational Geostatistics Deartment of Civil &
More informationAn Investigation on the Numerical Ill-conditioning of Hybrid State Estimators
An Investigation on the Numerical Ill-conditioning of Hybrid State Estimators S. K. Mallik, Student Member, IEEE, S. Chakrabarti, Senior Member, IEEE, S. N. Singh, Senior Member, IEEE Deartment of Electrical
More informationShadow Computing: An Energy-Aware Fault Tolerant Computing Model
Shadow Comuting: An Energy-Aware Fault Tolerant Comuting Model Bryan Mills, Taieb Znati, Rami Melhem Deartment of Comuter Science University of Pittsburgh (bmills, znati, melhem)@cs.itt.edu Index Terms
More informationUnderstanding and Using Availability
Understanding and Using Availability Jorge Luis Romeu, Ph.D. ASQ CQE/CRE, & Senior Member C. Stat Fellow, Royal Statistical Society Past Director, Region II (NY & PA) Director: Juarez Lincoln Marti Int
More informationMetrics Performance Evaluation: Application to Face Recognition
Metrics Performance Evaluation: Alication to Face Recognition Naser Zaeri, Abeer AlSadeq, and Abdallah Cherri Electrical Engineering Det., Kuwait University, P.O. Box 5969, Safat 6, Kuwait {zaery, abeer,
More informationScaling Multiple Point Statistics for Non-Stationary Geostatistical Modeling
Scaling Multile Point Statistics or Non-Stationary Geostatistical Modeling Julián M. Ortiz, Steven Lyster and Clayton V. Deutsch Centre or Comutational Geostatistics Deartment o Civil & Environmental Engineering
More informationCompletely Randomized Design
CHAPTER 4 Comletely Randomized Design 4.1 Descrition of the Design Chaters 1 to 3 introduced some basic concets and statistical tools that are used in exerimental design. In this and the following chaters,
More informationPER-PATCH METRIC LEARNING FOR ROBUST IMAGE MATCHING. Sezer Karaoglu, Ivo Everts, Jan C. van Gemert, and Theo Gevers
PER-PATCH METRIC LEARNING FOR ROBUST IMAGE MATCHING Sezer Karaoglu, Ivo Everts, Jan C. van Gemert, and Theo Gevers Intelligent Systems Lab, Amsterdam, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
More informationAn Analysis of Reliable Classifiers through ROC Isometrics
An Analysis of Reliable Classifiers through ROC Isometrics Stijn Vanderlooy s.vanderlooy@cs.unimaas.nl Ida G. Srinkhuizen-Kuyer kuyer@cs.unimaas.nl Evgueni N. Smirnov smirnov@cs.unimaas.nl MICC-IKAT, Universiteit
More informationTowards understanding the Lorenz curve using the Uniform distribution. Chris J. Stephens. Newcastle City Council, Newcastle upon Tyne, UK
Towards understanding the Lorenz curve using the Uniform distribution Chris J. Stehens Newcastle City Council, Newcastle uon Tyne, UK (For the Gini-Lorenz Conference, University of Siena, Italy, May 2005)
More informationOn split sample and randomized confidence intervals for binomial proportions
On slit samle and randomized confidence intervals for binomial roortions Måns Thulin Deartment of Mathematics, Usala University arxiv:1402.6536v1 [stat.me] 26 Feb 2014 Abstract Slit samle methods have
More informationRobustness of classifiers to uniform l p and Gaussian noise Supplementary material
Robustness of classifiers to uniform l and Gaussian noise Sulementary material Jean-Yves Franceschi Ecole Normale Suérieure de Lyon LIP UMR 5668 Omar Fawzi Ecole Normale Suérieure de Lyon LIP UMR 5668
More informationPlotting the Wilson distribution
, Survey of English Usage, University College London Setember 018 1 1. Introduction We have discussed the Wilson score interval at length elsewhere (Wallis 013a, b). Given an observed Binomial roortion
More informationLOGISTIC REGRESSION. VINAYANAND KANDALA M.Sc. (Agricultural Statistics), Roll No I.A.S.R.I, Library Avenue, New Delhi
LOGISTIC REGRESSION VINAANAND KANDALA M.Sc. (Agricultural Statistics), Roll No. 444 I.A.S.R.I, Library Avenue, New Delhi- Chairerson: Dr. Ranjana Agarwal Abstract: Logistic regression is widely used when
More informationPrinciples of Computed Tomography (CT)
Page 298 Princiles of Comuted Tomograhy (CT) The theoretical foundation of CT dates back to Johann Radon, a mathematician from Vienna who derived a method in 1907 for rojecting a 2-D object along arallel
More informationEvaluation of the critical wave groups method for calculating the probability of extreme ship responses in beam seas
Proceedings of the 6 th International Shi Stability Worsho, 5-7 June 207, Belgrade, Serbia Evaluation of the critical wave grous method for calculating the robability of extreme shi resonses in beam seas
More informationAN OPTIMAL CONTROL CHART FOR NON-NORMAL PROCESSES
AN OPTIMAL CONTROL CHART FOR NON-NORMAL PROCESSES Emmanuel Duclos, Maurice Pillet To cite this version: Emmanuel Duclos, Maurice Pillet. AN OPTIMAL CONTROL CHART FOR NON-NORMAL PRO- CESSES. st IFAC Worsho
More informationVIBRATION ANALYSIS OF BEAMS WITH MULTIPLE CONSTRAINED LAYER DAMPING PATCHES
Journal of Sound and Vibration (998) 22(5), 78 85 VIBRATION ANALYSIS OF BEAMS WITH MULTIPLE CONSTRAINED LAYER DAMPING PATCHES Acoustics and Dynamics Laboratory, Deartment of Mechanical Engineering, The
More informationSTA 250: Statistics. Notes 7. Bayesian Approach to Statistics. Book chapters: 7.2
STA 25: Statistics Notes 7. Bayesian Aroach to Statistics Book chaters: 7.2 1 From calibrating a rocedure to quantifying uncertainty We saw that the central idea of classical testing is to rovide a rigorous
More informationResearch Note REGRESSION ANALYSIS IN MARKOV CHAIN * A. Y. ALAMUTI AND M. R. MESHKANI **
Iranian Journal of Science & Technology, Transaction A, Vol 3, No A3 Printed in The Islamic Reublic of Iran, 26 Shiraz University Research Note REGRESSION ANALYSIS IN MARKOV HAIN * A Y ALAMUTI AND M R
More informationAn Analysis of TCP over Random Access Satellite Links
An Analysis of over Random Access Satellite Links Chunmei Liu and Eytan Modiano Massachusetts Institute of Technology Cambridge, MA 0239 Email: mayliu, modiano@mit.edu Abstract This aer analyzes the erformance
More informationUncorrelated Multilinear Discriminant Analysis with Regularization and Aggregation for Tensor Object Recognition
Uncorrelated Multilinear Discriminant Analysis with Regularization and Aggregation for Tensor Object Recognition Haiing Lu, K.N. Plataniotis and A.N. Venetsanooulos The Edward S. Rogers Sr. Deartment of
More informationAn Improved Generalized Estimation Procedure of Current Population Mean in Two-Occasion Successive Sampling
Journal of Modern Alied Statistical Methods Volume 15 Issue Article 14 11-1-016 An Imroved Generalized Estimation Procedure of Current Poulation Mean in Two-Occasion Successive Samling G. N. Singh Indian
More informationChapter 7 Sampling and Sampling Distributions. Introduction. Selecting a Sample. Introduction. Sampling from a Finite Population
Chater 7 and s Selecting a Samle Point Estimation Introduction to s of Proerties of Point Estimators Other Methods Introduction An element is the entity on which data are collected. A oulation is a collection
More informationOn the Estimation Of Population Mean Under Systematic Sampling Using Auxiliary Attributes
Oriental Journal of Physical Sciences Vol 1 (1 & ) 17 (016) On the Estimation Of Poulation Mean Under Systematic Samling Using Auxiliary Attributes Usman Shahzad Deartment of Mathematics Statistics PMAS
More informationImproved Capacity Bounds for the Binary Energy Harvesting Channel
Imroved Caacity Bounds for the Binary Energy Harvesting Channel Kaya Tutuncuoglu 1, Omur Ozel 2, Aylin Yener 1, and Sennur Ulukus 2 1 Deartment of Electrical Engineering, The Pennsylvania State University,
More informationSlides Prepared by JOHN S. LOUCKS St. Edward s s University Thomson/South-Western. Slide
s Preared by JOHN S. LOUCKS St. Edward s s University 1 Chater 11 Comarisons Involving Proortions and a Test of Indeendence Inferences About the Difference Between Two Poulation Proortions Hyothesis Test
More informationDeveloping A Deterioration Probabilistic Model for Rail Wear
International Journal of Traffic and Transortation Engineering 2012, 1(2): 13-18 DOI: 10.5923/j.ijtte.20120102.02 Develoing A Deterioration Probabilistic Model for Rail Wear Jabbar-Ali Zakeri *, Shahrbanoo
More informationEstimation of Separable Representations in Psychophysical Experiments
Estimation of Searable Reresentations in Psychohysical Exeriments Michele Bernasconi (mbernasconi@eco.uninsubria.it) Christine Choirat (cchoirat@eco.uninsubria.it) Raffaello Seri (rseri@eco.uninsubria.it)
More informationSAS for Bayesian Mediation Analysis
Paer 1569-2014 SAS for Bayesian Mediation Analysis Miočević Milica, Arizona State University; David P. MacKinnon, Arizona State University ABSTRACT Recent statistical mediation analysis research focuses
More informationOn Fractional Predictive PID Controller Design Method Emmanuel Edet*. Reza Katebi.**
On Fractional Predictive PID Controller Design Method Emmanuel Edet*. Reza Katebi.** * echnology and Innovation Centre, Level 4, Deartment of Electronic and Electrical Engineering, University of Strathclyde,
More informationMAKING WALD TESTS WORK FOR. Juan J. Dolado CEMFI. Casado del Alisal, Madrid. and. Helmut Lutkepohl. Humboldt Universitat zu Berlin
November 3, 1994 MAKING WALD TESTS WORK FOR COINTEGRATED VAR SYSTEMS Juan J. Dolado CEMFI Casado del Alisal, 5 28014 Madrid and Helmut Lutkeohl Humboldt Universitat zu Berlin Sandauer Strasse 1 10178 Berlin,
More informationRadial Basis Function Networks: Algorithms
Radial Basis Function Networks: Algorithms Introduction to Neural Networks : Lecture 13 John A. Bullinaria, 2004 1. The RBF Maing 2. The RBF Network Architecture 3. Comutational Power of RBF Networks 4.
More informationElliptic Curves and Cryptography
Ellitic Curves and Crytograhy Background in Ellitic Curves We'll now turn to the fascinating theory of ellitic curves. For simlicity, we'll restrict our discussion to ellitic curves over Z, where is a
More informationarxiv: v2 [stat.me] 3 Nov 2014
onarametric Stein-tye Shrinkage Covariance Matrix Estimators in High-Dimensional Settings Anestis Touloumis Cancer Research UK Cambridge Institute University of Cambridge Cambridge CB2 0RE, U.K. Anestis.Touloumis@cruk.cam.ac.uk
More informationWolfgang POESSNECKER and Ulrich GROSS*
Proceedings of the Asian Thermohysical Proerties onference -4 August, 007, Fukuoka, Jaan Paer No. 0 A QUASI-STEADY YLINDER METHOD FOR THE SIMULTANEOUS DETERMINATION OF HEAT APAITY, THERMAL ONDUTIVITY AND
More informationEvaluating Circuit Reliability Under Probabilistic Gate-Level Fault Models
Evaluating Circuit Reliability Under Probabilistic Gate-Level Fault Models Ketan N. Patel, Igor L. Markov and John P. Hayes University of Michigan, Ann Arbor 48109-2122 {knatel,imarkov,jhayes}@eecs.umich.edu
More informationMethods for detecting fatigue cracks in gears
Journal of Physics: Conference Series Methods for detecting fatigue cracks in gears To cite this article: A Belšak and J Flašker 2009 J. Phys.: Conf. Ser. 181 012090 View the article online for udates
More informationUsing the Divergence Information Criterion for the Determination of the Order of an Autoregressive Process
Using the Divergence Information Criterion for the Determination of the Order of an Autoregressive Process P. Mantalos a1, K. Mattheou b, A. Karagrigoriou b a.deartment of Statistics University of Lund
More informationUncorrelated Multilinear Discriminant Analysis with Regularization and Aggregation for Tensor Object Recognition
TNN-2007-P-0332.R1 1 Uncorrelated Multilinear Discriminant Analysis with Regularization and Aggregation for Tensor Object Recognition Haiing Lu, K.N. Plataniotis and A.N. Venetsanooulos The Edward S. Rogers
More informationThe Binomial Approach for Probability of Detection
Vol. No. (Mar 5) - The e-journal of Nondestructive Testing - ISSN 45-494 www.ndt.net/?id=7498 The Binomial Aroach for of Detection Carlos Correia Gruo Endalloy C.A. - Caracas - Venezuela www.endalloy.net
More informationGENERATING FUZZY RULES FOR PROTEIN CLASSIFICATION E. G. MANSOORI, M. J. ZOLGHADRI, S. D. KATEBI, H. MOHABATKAR, R. BOOSTANI AND M. H.
Iranian Journal of Fuzzy Systems Vol. 5, No. 2, (2008). 21-33 GENERATING FUZZY RULES FOR PROTEIN CLASSIFICATION E. G. MANSOORI, M. J. ZOLGHADRI, S. D. KATEBI, H. MOHABATKAR, R. BOOSTANI AND M. H. SADREDDINI
More informationCharacterizing the Behavior of a Probabilistic CMOS Switch Through Analytical Models and Its Verification Through Simulations
Characterizing the Behavior of a Probabilistic CMOS Switch Through Analytical Models and Its Verification Through Simulations PINAR KORKMAZ, BILGE E. S. AKGUL and KRISHNA V. PALEM Georgia Institute of
More informationMODEL-BASED MULTIPLE FAULT DETECTION AND ISOLATION FOR NONLINEAR SYSTEMS
MODEL-BASED MULIPLE FAUL DEECION AND ISOLAION FOR NONLINEAR SYSEMS Ivan Castillo, and homas F. Edgar he University of exas at Austin Austin, X 78712 David Hill Chemstations Houston, X 77009 Abstract A
More informationSeries Handout A. 1. Determine which of the following sums are geometric. If the sum is geometric, express the sum in closed form.
Series Handout A. Determine which of the following sums are geometric. If the sum is geometric, exress the sum in closed form. 70 a) k= ( k ) b) 50 k= ( k )2 c) 60 k= ( k )k d) 60 k= (.0)k/3 2. Find the
More informationModeling and Estimation of Full-Chip Leakage Current Considering Within-Die Correlation
6.3 Modeling and Estimation of Full-Chi Leaage Current Considering Within-Die Correlation Khaled R. eloue, Navid Azizi, Farid N. Najm Deartment of ECE, University of Toronto,Toronto, Ontario, Canada {haled,nazizi,najm}@eecg.utoronto.ca
More informationA Recursive Block Incomplete Factorization. Preconditioner for Adaptive Filtering Problem
Alied Mathematical Sciences, Vol. 7, 03, no. 63, 3-3 HIKARI Ltd, www.m-hiari.com A Recursive Bloc Incomlete Factorization Preconditioner for Adative Filtering Problem Shazia Javed School of Mathematical
More informationSTART Selected Topics in Assurance
START Selected Toics in Assurance Related Technologies Table of Contents Introduction Statistical Models for Simle Systems (U/Down) and Interretation Markov Models for Simle Systems (U/Down) and Interretation
More informationAnalysis of some entrance probabilities for killed birth-death processes
Analysis of some entrance robabilities for killed birth-death rocesses Master s Thesis O.J.G. van der Velde Suervisor: Dr. F.M. Sieksma July 5, 207 Mathematical Institute, Leiden University Contents Introduction
More informationA Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split
A Bound on the Error of Cross Validation Using the Aroximation and Estimation Rates, with Consequences for the Training-Test Slit Michael Kearns AT&T Bell Laboratories Murray Hill, NJ 7974 mkearns@research.att.com
More information16. CHARACTERISTICS OF SHOCK-WAVE UNDER LORENTZ FORCE AND ENERGY EXCHANGE
16. CHARACTERISTICS OF SHOCK-WAVE UNDER LORENTZ FORCE AND ENERGY EXCHANGE H. Yamasaki, M. Abe and Y. Okuno Graduate School at Nagatsuta, Tokyo Institute of Technology 459, Nagatsuta, Midori-ku, Yokohama,
More informationECON 4130 Supplementary Exercises 1-4
HG Set. 0 ECON 430 Sulementary Exercises - 4 Exercise Quantiles (ercentiles). Let X be a continuous random variable (rv.) with df f( x ) and cdf F( x ). For 0< < we define -th quantile (or 00-th ercentile),
More information