ISSN X Reliability of linear and circular consecutive-kout-of-n systems with shock model

Size: px
Start display at page:

Download "ISSN X Reliability of linear and circular consecutive-kout-of-n systems with shock model"

Transcription

1 Afrka Statstka Vol. 101, 2015, pages DOI: Afrka Statstka ISSN X Relablty of lnear and crcular consecutve-kout-of-n systems wth shock model Besma Bennour and Soher Belalou, Department of Mathematcs and Informatcs, Larb Ben M hd unversty, Oum El Bouagh, Algera Department of Mathematcs, Constantne 1 Unversty, Algera Receved January 31, 2015; Accepted October 14, 2015 Copyrght c 2015, Afrka Statstka. All rghts reserved Abstract. A consecutve k-out-of-n system conssts of an ordered sequence of n components, such that the system functons f and only f at least k k n consecutve components functon. The system s called lnear L or crcular C dependng on whether the components are arranged on a straght lne or form a crcle. In the frst part, we use a shock model to obtan the relablty functon of consecutve-k-out-of-n systems wth dependent and nondentcal components. In the second part, we treat some numercal examples to show the derve results and deduce the falure rate of each component and the system. Résumé. Un système k-consécutfs-parm-n est un système consttué de n composants, tel que ce système fonctonne s et seulement s au mons k k n composants consécutfs fonctonnent. Le système est dt lnéare L ou crculare C suvant la dsposton des composants en lgne ou en cercle. Dans la premère secton, en utlsant le modèle de chocs, on établt la fablté du système en queston ayant des composants dépendants et non dentques. Dans la deuxème secton, on trate des exemples numérques qu llustrent les résultats obtenus tout en dédusant le taux de panne de chaque composant et du système. Key words: Lnear and crcular consecutve-k-out-of-n system; Felablty functon; Falure rate; Shock model. AMS 2010 Mathematcs Subject Classfcaton :62N05; 68M15; 68M20; 90B25; 60K Introducton In some envronments, the falure of the system depends not only on the tme, but also upon the number of random shocks. So many applcatons n relablty analyss can be Correspondng author Soher Belalou : s belalou@yahoo.fr Besma Bennour: besma bennour@yahoo.fr

2 B. Bennour, and S. Belalou, Afrka Statstka, Vol. 101, 2015, pages Relablty of lnear and crcular consecutve-k-out-of-n systems wth shock model. 796 descrbed by shock models. Shocks may refer for example to damage caused to bologcal organs by llness or envronmental causes of damage actng on a techncal system, see e.g Hameed and Proschan Also, as an example, a press machne produces external frames n automobles or refrgerators. The machne can fal due to unexpected causes such as accdental changes n the temperature or electrcal voltage, defectve raw materals, or errors by human operators. In the lterature, the concept of models shock on systems was treated by many researchers, we can quote some of them. Marshall and Olkn 1967 consdered forms of shock to deduce the bvarate exponental dstrbuton. Where, ther man objectve s stated that two components are subjected to shocks from three dfferent ndependent sources. One shock destroys one component and the thrd shock destroys both components. Shock model s also used by Grabsk and Sarhan 1995 and Sarhan 1996 to obtan the relablty measures estmatons for seres and parallel systems wth two nonndependent and nondentcal components. They assumed that the tmes at whch the shocks occur are exponentally dstrbuted. Sarhan, A.M. and Abouammoh 2000 used the shock model to derve the relablty functon of k-out-of-n systems wth nonndependent and nondentcal components. They assumed that a system s subjected to n + m ndependent types of shocks. Lu et al proposed a model to evaluate the relablty functon of seres and parallel systems wth degradaton and random shocks. In ther model, the system s assumed to be faled when nternal degradaton or cumulatve damage from random shocks exceed random lfe thresholds. There are other several types of the shock models whch have been consdered for the falure of a system as: extreme shock model, cumulatve shock model, run shock model, or δ-shock model. For example, δ-shock model s based on the length of the tme between successve shocks. In ths model, the system fals when the tme between two consecutve shocks falls below a fxed threshold δ. Ths shock model has been studed by L and Zhao 2007 and Erylmaz The man objectve n ths paper s to use a shock model to obtan the relablty functon of consecutve-k-out-of-n systems, whch have a wde range of applcatons; e.g: telecommuncatons, gas and ol ppelnes, transport network...etc, wth dependent and nondentcal components n the two topologes, lnear and crcular confguratons. Then to deduce the falure rate of the system and also the relablty and the falure rate of each component n the system. In the end, we treat some examples to llustrate the provded results. 2. Notatons and assumptons Notatons n: number of components n a system. k: the mnmum number of consecutve components requred to be good for the system to be good. n + mm 1: number of ndependent sources of shocks. s : the source, c : the component. S = {s 1,..., s n,..., s n+m }: the set of all sources. C: the set of sources whch destroy component.

3 B. Bennour, and S. Belalou, Afrka Statstka, Vol. 101, 2015, pages Relablty of lnear and crcular consecutve-k-out-of-n systems wth shock model. 797 { n k + 1 In the lnear case J = n In the crcular case I = {c,..., c +k 1 }, = 1,..., J, the th mnmal path. S = +k 1 l= C l, = 1,..., J. { S j S f j = 1 = j l=1 S l f j = 2,..., J where: 1 1 < < l < < j J. U : the random tme of shock from s. Q t = PU t: the dstrbuton functon of U. Q t = PU > t. T = mn j C U j : the lfetme of the component, = 1, 2,..., n. A : the event {T > t}. B = +k 1 j= A j. p: denotes L lnear or C crcular. T p : the lfetme of consecutve-k-out-of-n system. R p t = PT p > t: the relablty functon of consecutve-k -out-of-n system. λ p t = R p t R pt : the falure rate of consecutve-k-out-of-n system. Assumptons 1. The T, = 1,..., n, are nonndependent and nondentcal dstrbuted. 2. The system s subjected to n + m ndependent sources of shocks. 3. s destroys c, = 1,..., n. At least one shock from the remanng m sources destroys all components, and shocks from the other sources m 1 destroy a group of components. The system s subjected to a set of varous shocks, where a shock from source occurs at a random tme U. The random varables U, = 1,..., n + m, are ndependent. 3. Relablty of Consecutve-k-out-of-n System In ths secton, we consder both lnear and crcular consecutve-k -out-of- n systems wth dependent and nondentcal components. Defnton 1. A consecutve k-out-of-n system conssts of n lnearly or crcularly arranged components, ths system works f and only f at least k consecutve components work. In other words, there s at least a mnmal path whch s workng. Defnton 2. A mnmal path vector s a set of mnmum number of components n workng state whch ensures the system s functonng. In the lnear case, we have: I1 = {c 1, c 2,..., c k 1, c k } I2 = {c 2, c 3,..., c k, c k+1 }. In k + 1 = {c n k+1, c n k+2,..., c n 1, c n }

4 B. Bennour, and S. Belalou, Afrka Statstka, Vol. 101, 2015, pages Relablty of lnear and crcular consecutve-k-out-of-n systems wth shock model. 798 But n the crcular case, we have: Where: c n+ = c, = 1,..., k 1. I1 = {c 1, c 2,..., c k 1, c k } I2 = {c 2, c 3,..., c k, c k+1 }. In k + 1 = {c n k+1, c n k+2,..., c n 1, c n } In k + 2 = {c n k+2, c n k+3,..., c n, c n+1 } In k + 3 = {c n k+3, c n k+4,..., c n+1, c n+2 }. In = {c n, c n+1,..., c n+k 2, c n+k 1 } Theorem 1. Let R p t be the relablty functon of consecutve-k-out-of-n systems, and T p denotes ts lfetme. We have: J [ } R p t = { 1 j 1 Q l t 1 j=1 If p = L, then J = n k + 1. If p = C, then J = n. 1 1<...< j J l S j Proof. The lnear or crcular consecutve k-out-of-n system works f at least k consecutve components work. Then, we have: R p t = PT p > t = P J =1 {T > t, T +1 > t,..., T +k 1 > t} = P J =1 { +k 1 j= A j } = P J =1 B we apply the addton theorem for J events: J R p t = P B P B 1 B 2 where, + =1 1 1< 2< 3 J 1 1< 2 J P B 1 B 2 B J 1 P B 1 B 2 B J P B = P T > t, T +1 > t,..., T +k 1 > t = P mn U l > t, mn U l > t,..., mn U l > t l C l C +1 l C +k 1 = P U l > t l C, U l > t l C +1,..., U l > t l C +k 1 = P {U l > t, l C C C +k 1 } = P { mn U l > t} l C C +k 1 = P l +k 1 j= C j Ul > t = l S Q l t = l S 1 2 Q l t 3

5 B. Bennour, and S. Belalou, Afrka Statstka, Vol. 101, 2015, pages Relablty of lnear and crcular consecutve-k-out-of-n systems wth shock model. 799 Smlarly, and P B 1 B 2 = P B 1 B 2 B 3 = l S 2 Q l t 4 l S 3 Q l t 5 P B 1 B 2 B J = Q l t 6 Substtutng the equatons 3, 4, 5, and 6 nto equaton 2 we obtan: l S R p t = + = J =1 l S 1 1 1< 2< 3 J Q l t l S 3 1 1< 2 J J { 1 j 1 j=1 l S 2 Q l t Q l t J 1 l S Q l t 1 1<...< j J [ l S j } Q l t Numercal examples 4.1. Lnear consecutve-k-out-of-n system In partcular t s assumed that: A1 k > 1, n, and s = 2n + 1. A2 s destroys c, = 1,..., n. A3 the sources between n and 2n + 1 destroy k consecutve components: s n+1 destroys {c 1,..., c k } = I1.... s 2n k+1 destroys {c n k+1,..., c n } = In k + 1. s 2n k+2 destroys {c n k+2,..., c n, c 1 }.... s 2n destroys {c n, c 1,..., c k 1 }. A4 s 2n+1 destroys all components. A5 the random varables U, = 1, 2,..., 2n + 1 are Webull dstrbuted wth parameters α, β respectvely: Q t = exp α t β Example 1: Lnear consecutve-2-out-of-3 system We consder a lnear consecutve-2-out-of-3 system and the assumptons A1-A5 are satsfed, we have: S = {s 1,..., s 7 }

6 B. Bennour, and S. Belalou, Afrka Statstka, Vol. 101, 2015, pages Relablty of lnear and crcular consecutve-k-out-of-n systems wth shock model. 800 C 1 = {s 1, s 4, s 6, s 7 }, C 2 = {s 2, s 4, s 5, s 7 }, C 3 = {s 3, s 5, s 6, s 7 } I1 = {c 1, c 2 }, I2 = {c 2, c 3 }. S 1 = 2 j=1 C j = S \ {s 3 } = S 1 1, S 2 = 3 j=2 C j = S \ {s 1 } = S 1 2 S 2 = 2 l=1 S l = S 1 S 2 = S 1 S 2 = S, where 1 1 < 2 2. Fg. 1. the lnear consecutve-2-out-of-3 system and 7 sources Usng equaton 1, t follows that: R L t = 2 { 1 j 1 j=1 = l S 1 1 Q l t + 1 l 7 1 1<...< j 2 l S 1 2 = exp [ l S j Q l t l S Q l t l {1,3} and λ L t; the falure rate of the lnear system s gven by: λ L t = 1 l 7 } Q l t exp, l {1,3} α l β l t βl 1 α lβ l t βl 1 exp 1 l {1,3} exp α lt β l 1 1 In general, f n = k + 1, then: R L t = exp l {1,n} exp 8 λ L t = l {1,n} α l β l t βl 1 α lβ l t βl 1 exp 1 l {1,n} exp α lt β. 9 l 1 1

7 B. Bennour, and S. Belalou, Afrka Statstka, Vol. 101, 2015, pages Relablty of lnear and crcular consecutve-k-out-of-n systems wth shock model. 801 Example 2: Lnear consecutve-3-out-of-5 system For ths system and the assumptons A1-A5, we have: S = {s 1,..., s 11 }. C 1 = {s 1, s 6, s 9, s 10, s 11 }, C 2 = {s 2, s 6, s 7, s 10, s 11 }, C 3 = {s 3, s 6, s 7, s 8, s 11 }, C 4 = {s 4, s 7, s 8, s 9, s 11 }, C 5 = {s 5, s 8, s 9, s 10, s 11 }. I1 = {c 1, c 2, c 3 }, I2 = {c 2, c 3, c 4 }, I3 = {c 3, c 4, c 5 }. S 1 = S \ {s 4, s 5 } = S 1 1, S 2 = S \ {s 1, s 5 } = S 1 2, S 3 = S \ {s 1, s 2 } = S 1 3. S 2 = 2 l=1 S l = S 1 S 2, where 1 1 < 2 3, then: S 2 = S 1 S 2 = S \ {s 5 } S 1 S 3 = S S 2 S 3 = S \ {s 1 } S 3 = 3 l=1 S l, where 1 1 < 2 < 3 3, then: S 3 = S. Usng equaton 1, we obtan: R L t = exp 1 l 11 l 5 + exp 2 l 11 l {1,4} exp [ exp α 2 t β2, and the the falure rate of the system s as followng: λ L t = exp + [ l {1,4} 2 l 11 1 l 11 l 5 In general, f n = k + 2, then: 1 l 11 l 5 α l β l t β l 1 l {1,4} α l β l t βl 1 exp 1 exp exp 2 l 11 α l β l t β l 1 exp α 2 t β2 + α 2 β 2 t β2 1 exp α 2 t β2 1 R L t = exp l n + exp 2 l 2n+1 l {1,n 1} exp [ exp α 2 t β2, 10

8 B. Bennour, and S. Belalou, Afrka Statstka, Vol. 101, 2015, pages Relablty of lnear and crcular consecutve-k-out-of-n systems wth shock model. 802 and: λ L t = exp + l {1,n 1} [ 2 l 2n+1 l n l n α l β l t β l 1 l {1,n 1} α l β l t βl 1 exp 1 exp 2 l 2n+1 exp α l β l t β l 1 exp α 2 t β2 + α 2 β 2 t β2 1 exp α 2 t β Example 3: Lnear consecutve-5-out-of-8 system We consder a lnear consecutve-5-out-of-8 system and the assumptons A1-A5 are satsfed, we have: S = {s 1,..., s 17 }. C 1 = {s 1, s 9, s 13, s 14, s 15, s 16, s 17 }, C 2 = {s 2, s 9, s 10, s 14, s 15, s 16, s 17 }, C 3 = {s 3, s 9, s 10, s 11, s 15, s 16, s 17 }, C 4 = {s 4, s 9, s 10, s 11, s 12, s 16, s 17 }, C 5 = {s 5, s 9, s 10, s 11, s 12, s 13, s 17 }, C 6 = {s 6, s 10, s 11, s 12, s 13, s 14, s 17 }, C 7 = {s 7, s 11, s 12, s 13, s 14, s 15, s 17 }, C 8 = {s 8, s 12, s 13, s 14, s 15, s 16, s 17 }. I1 = {c 1,..., c 5 }, I2 = {c 2,..., c 6 }, I3 = {c 3,..., c 7 }, I4 = {c 4,..., c 8 }. S 1 = S \ {s 6, s 7, s 8 } = S 1 1, S 2 = S \ {s 1, s 7, s 8 } = S 1 2, S 3 = S \ {s 1, s 2, s 8 } = S 1 3, S 4 = S \ {s 1, s 2, s 3 } = S 1 4, S 2 = 2 l=1 S l = S 1 S 2, where 1 1 < 2 4, then: S 1 S 2 = S \ {s 7, s 8 } S 1 S 3 = S \ {s 8 } S 2 S = 1 S 4 = S S 2 S 3 = S \ {s 1, s 8 } S 2 S 4 = S \ {s 1 } S 3 S 4 = S \ {s 1, s 2 }. S 1 S 2 S 3 = S \ {s 8 } S 3 = 3 l=1 S l = S 1 S 2 S 3 = S 4 = 4 l=1 S l = S. Usng equaton 1, we have: R L t = exp 1 l 17 l 8,l 7 + exp 3 l 17 S 1 S 2 S 4 = S S 1 S 3 S 4 = S S 2 S 3 S 4 = S \ {s 1 } l {1,6} exp exp

9 B. Bennour, and S. Belalou, Afrka Statstka, Vol. 101, 2015, pages Relablty of lnear and crcular consecutve-k-out-of-n systems wth shock model. 803 and so: λ L t = exp l {1,6} 1 l 17 l 7 exp + 1 l 17 l 8,l 7 α l β l t β l 1 exp + 3 l 17 3 l 17 α l β l t β l 1 exp 1. l {1,6} α l β l t β l 1 α l β l t β l 1 exp 1 exp In general, f n = k + 3, then: Rt = exp l n,l n 1 + exp 3 l 2n+1 l {1,n 2} exp exp 12 λ L t = exp l {1,n 2} l n 1 exp + l n,l n 1 α l β l t β l 1 exp + 3 l 2n+1 3 l 2n+1 l {1,n 2} α l β l t β l 1 α l β l t β l 1 exp 1 exp α l β l t β l 1 exp Remark 1. For all precedent cases, let R t et λ t, = 1,..., n, denote respectvely the relablty functon and the falure rate of the component, whch are gven by the followng expressons: R t = exp, λ t = α l β l t βl 1. l S l S 4.2. Crcular consecutve-k-out-of-n system Example 4: Crcular consecutve-2-out-of-3 system We consder a crcular consecutve-2-out-of-3 system and 7 ndependent sources act on the system see fg.2. S = {s 1, s 2, s 3, s 4, s 5, s 6, s 7 } C 1 = {s 1, s 4, s 6, s 7 }, C 2 = {s 2, s 4, s 5, s 7 }, C 3 = {s 3, s 5, s 6, s 7 },

10 B. Bennour, and S. Belalou, Afrka Statstka, Vol. 101, 2015, pages Relablty of lnear and crcular consecutve-k-out-of-n systems wth shock model. 804 I1 = {c 1, c 2 }, I2 = {c 2, c 3 }, I3 = {c 3, c 1 }. S 1 = S \ {s 3 } = S 1 1, S 2 = S \ {s 1 } = S 1 2, S 3 = S \ {s 2 } = S 1 S 1 S 2 = S S 2 = 2 l=1 S l = S 1 S 2 = S 1 S 3 = S S 2 S 3 = S S 3 = S. 3, Fg. 2. The crcular consecutve-2-out-of-3 system and 7 sources U Webα, β, = 1;... ; 7, and usng equaton 1, t follows that: R C t = exp 1 l 7 1 l 3 then λ C t; the falure rate of the crcular system, s gven by: λ C t = 1 l 7 exp 1 2, 1 l 3 α l β l t βl 1 α lβ l t βl 1 exp 1 1 l 3 exp α lt β. l 1 2 Example 5: Crcular consecutve-3 -out-of-5 system We consder a crcular consecutve-3-out-of-5 system and 11 ndependent sources act on the system. S = {s 1,..., s 11 }. C 1 = {s 1, s 6, s 9, s 10, s 11 }, C 2 = {s 2, s 6, s 7, s 10, s 11 }, C 3 = {s 3, s 6, s 7, s 8, s 11 }, C 4 = {s 4, s 7, s 8, s 9, s 11 }, C 5 = {s 5, s 8, s 9, s 10, s 11 }. I1 = {c 1, c 2, c 3 }, I2 = {c 2, c 3, c 4 }, I3 = {c 3, c 4, c 5 }, I4 = {c 4, c 5, c 1 }, I5 = {c 5, c 1, c 2 }. S 1 = S \ {s 4, s 5 }, S 2 = S \ {s 1, s 5 }, S 3 = S \ {s 1, s 2 }, S 4 = S \ {s 2, s 3 }, S 5 = S \ {s 3, s 4 }.

11 B. Bennour, and S. Belalou, Afrka Statstka, Vol. 101, 2015, pages Relablty of lnear and crcular consecutve-k-out-of-n systems wth shock model. 805 S 2 = 2 l=1 S l = S 1 S 2, where 1 1 < 2 5, then: S 1 S 2 = S \ {s 5 } S 1 S 5 = S \ {s 4 } S 2 S = 2 S 3 = S \ {s 1 } S 3 S 4 = S \ {s 2 } S 4 S 5 = S \ {s 3 } S for the other cases. S 3 = 3 l=1 S l = S, where 1 1 < 2 < 3 5. S 4 = 4 l=1 S l = S, where 1 1 <... < 4 5. S 5 = S. Usng equaton 1, we obtan: R C t = Q l t + 1 l 5 l {,+1} = 1 l 5 exp l {,+1} 1 l 11 Q l t [1 1 l 5 + exp 1 l 11 Q l t 1, [1 where Q 6 t = Q 1 t 1 l 5 exp 1. References Erylmaz, S., Generalzed δ-shock model va runs. Statstcal and Probablty Letters 82, Grabsk, F. and Sarhan, A.M., Bayesan estmaton of two non ndependant components exponental seres system. Zagadnena Eksploatacj Maszn Zeszyt Hameed. A.M.S. and Proschan, F., Nonstatonary shock models, Stock Proc Appl, L, Z.H. and Zhao, P., Relablty analyss on the δ -shock model of complex systems. LEEE Trans. On Relablty. 56, Lu, Y., Huang H.Z. and Pham, H., Relablty evaluaton of systems wth degradaton and random shocks. Proceedngs of the Relablty and Mantanablty Symposum. Las Vegas, USA: IEEE, Marshall, A. and I.Olkn, I., A multvarate exponental dstrbuton. J Amer. statst. Assoc Sarhan, A.M. and Abouammoh, A.M., Relablty of k-out-of-n nonreparable systems wth nonndependent components subjected to common shocks. Mcroelectroncs Relablty. 41, Sarhan, A.M., Bayesan estmaton n relablty theory. PH.D. dssertaton, Insttute of Mathematcs, Gdansk Unversty, Gdansk, Poland.

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

System in Weibull Distribution

System in Weibull Distribution Internatonal Matheatcal Foru 4 9 no. 9 94-95 Relablty Equvalence Factors of a Seres-Parallel Syste n Webull Dstrbuton M. A. El-Dacese Matheatcs Departent Faculty of Scence Tanta Unversty Tanta Egypt eldacese@yahoo.co

More information

The optimal delay of the second test is therefore approximately 210 hours earlier than =2.

The optimal delay of the second test is therefore approximately 210 hours earlier than =2. THE IEC 61508 FORMULAS 223 The optmal delay of the second test s therefore approxmately 210 hours earler than =2. 8.4 The IEC 61508 Formulas IEC 61508-6 provdes approxmaton formulas for the PF for smple

More information

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method

More information

Case Study of Cascade Reliability with weibull Distribution

Case Study of Cascade Reliability with weibull Distribution ISSN: 77-3754 ISO 900:008 Certfed Internatonal Journal of Engneerng and Innovatve Technology (IJEIT) Volume, Issue 6, June 0 Case Study of Cascade Relablty wth webull Dstrbuton Dr.T.Shyam Sundar Abstract

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Statistical inference for generalized Pareto distribution based on progressive Type-II censored data with random removals

Statistical inference for generalized Pareto distribution based on progressive Type-II censored data with random removals Internatonal Journal of Scentfc World, 2 1) 2014) 1-9 c Scence Publshng Corporaton www.scencepubco.com/ndex.php/ijsw do: 10.14419/jsw.v21.1780 Research Paper Statstcal nference for generalzed Pareto dstrbuton

More information

CS-433: Simulation and Modeling Modeling and Probability Review

CS-433: Simulation and Modeling Modeling and Probability Review CS-433: Smulaton and Modelng Modelng and Probablty Revew Exercse 1. (Probablty of Smple Events) Exercse 1.1 The owner of a camera shop receves a shpment of fve cameras from a camera manufacturer. Unknown

More information

Double Acceptance Sampling Plan for Time Truncated Life Tests Based on Transmuted Generalized Inverse Weibull Distribution

Double Acceptance Sampling Plan for Time Truncated Life Tests Based on Transmuted Generalized Inverse Weibull Distribution J. Stat. Appl. Pro. 6, No. 1, 1-6 2017 1 Journal of Statstcs Applcatons & Probablty An Internatonal Journal http://dx.do.org/10.18576/jsap/060101 Double Acceptance Samplng Plan for Tme Truncated Lfe Tests

More information

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation Nonl. Analyss and Dfferental Equatons, ol., 4, no., 5 - HIKARI Ltd, www.m-har.com http://dx.do.org/.988/nade.4.456 Asymptotcs of the Soluton of a Boundary alue Problem for One-Characterstc Dfferental Equaton

More information

ABSTRACT

ABSTRACT RELIABILITY AND SENSITIVITY ANALYSIS OF THE K-OUT-OF-N:G WARM STANDBY PARALLEL REPAIRABLE SYSTEM WITH REPLACEMENT AT COMMON-CAUSE FAILURE USING MARKOV MODEL M. A. El-Damcese 1 and N. H. El-Sodany 2 1 Mathematcs

More information

Hydrological statistics. Hydrological statistics and extremes

Hydrological statistics. Hydrological statistics and extremes 5--0 Stochastc Hydrology Hydrologcal statstcs and extremes Marc F.P. Berkens Professor of Hydrology Faculty of Geoscences Hydrologcal statstcs Mostly concernes wth the statstcal analyss of hydrologcal

More information

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth

More information

A new Approach for Solving Linear Ordinary Differential Equations

A new Approach for Solving Linear Ordinary Differential Equations , ISSN 974-57X (Onlne), ISSN 974-5718 (Prnt), Vol. ; Issue No. 1; Year 14, Copyrght 13-14 by CESER PUBLICATIONS A new Approach for Solvng Lnear Ordnary Dfferental Equatons Fawz Abdelwahd Department of

More information

Convexity preserving interpolation by splines of arbitrary degree

Convexity preserving interpolation by splines of arbitrary degree Computer Scence Journal of Moldova, vol.18, no.1(52), 2010 Convexty preservng nterpolaton by splnes of arbtrary degree Igor Verlan Abstract In the present paper an algorthm of C 2 nterpolaton of dscrete

More information

A note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights

A note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights ACTA ET COMMENTATIONES UNIVERSITATIS TARTUENSIS DE MATHEMATICA Volume 7, Number 2, December 203 Avalable onlne at http://acutm.math.ut.ee A note on almost sure behavor of randomly weghted sums of φ-mxng

More information

A random variable is a function which associates a real number to each element of the sample space

A random variable is a function which associates a real number to each element of the sample space Introducton to Random Varables Defnton of random varable Defnton of of random varable Dscrete and contnuous random varable Probablty blt functon Dstrbuton functon Densty functon Sometmes, t s not enough

More information

Neryškioji dichotominių testo klausimų ir socialinių rodiklių diferencijavimo savybių klasifikacija

Neryškioji dichotominių testo klausimų ir socialinių rodiklių diferencijavimo savybių klasifikacija Neryškoj dchotomnų testo klausmų r socalnų rodklų dferencjavmo savybų klasfkacja Aleksandras KRYLOVAS, Natalja KOSAREVA, Julja KARALIŪNAITĖ Technologcal and Economc Development of Economy Receved 9 May

More information

A Hybrid Variational Iteration Method for Blasius Equation

A Hybrid Variational Iteration Method for Blasius Equation Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method

More information

Tail Dependence Comparison of Survival Marshall-Olkin Copulas

Tail Dependence Comparison of Survival Marshall-Olkin Copulas Tal Dependence Comparson of Survval Marshall-Olkn Copulas Hajun L Department of Mathematcs and Department of Statstcs Washngton State Unversty Pullman, WA 99164, U.S.A. lh@math.wsu.edu January 2006 Abstract

More information

Manpower Planning Process with Two Groups Using Statistical Techniques

Manpower Planning Process with Two Groups Using Statistical Techniques Internatonal Journal of Mathematcs and Statstcs Inventon (IJMSI) E-ISSN: 3 4767 P-ISSN: 3-4759 Volume Issue 8 August. 04 PP-57-6 Manpower Plannng Process wth Two Groups Usng Statstcal Technques Dr. P.

More information

Accelerated Life Testing in Interference Models with Monte-Carlo Simulation

Accelerated Life Testing in Interference Models with Monte-Carlo Simulation Global Journal of ure and ppled Mathematcs. ISSN 0973-768 Volume 3, Number (07), pp. 733-748 esearch Inda ublcatons http://www.rpublcaton.com ccelerated Lfe Testng n Interference Models wth Monte-Carlo

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION Advanced Mathematcal Models & Applcatons Vol.3, No.3, 2018, pp.215-222 ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EUATION

More information

Introduction to Random Variables

Introduction to Random Variables Introducton to Random Varables Defnton of random varable Defnton of random varable Dscrete and contnuous random varable Probablty functon Dstrbuton functon Densty functon Sometmes, t s not enough to descrbe

More information

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

Games of Threats. Elon Kohlberg Abraham Neyman. Working Paper

Games of Threats. Elon Kohlberg Abraham Neyman. Working Paper Games of Threats Elon Kohlberg Abraham Neyman Workng Paper 18-023 Games of Threats Elon Kohlberg Harvard Busness School Abraham Neyman The Hebrew Unversty of Jerusalem Workng Paper 18-023 Copyrght 2017

More information

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran

More information

On the Multicriteria Integer Network Flow Problem

On the Multicriteria Integer Network Flow Problem BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 5, No 2 Sofa 2005 On the Multcrtera Integer Network Flow Problem Vassl Vasslev, Marana Nkolova, Maryana Vassleva Insttute of

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

A Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach

A Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach A Bayes Algorthm for the Multtask Pattern Recognton Problem Drect Approach Edward Puchala Wroclaw Unversty of Technology, Char of Systems and Computer etworks, Wybrzeze Wyspanskego 7, 50-370 Wroclaw, Poland

More information

The internal structure of natural numbers and one method for the definition of large prime numbers

The internal structure of natural numbers and one method for the definition of large prime numbers The nternal structure of natural numbers and one method for the defnton of large prme numbers Emmanul Manousos APM Insttute for the Advancement of Physcs and Mathematcs 3 Poulou str. 53 Athens Greece Abstract

More information

6.3.7 Example with Runga Kutta 4 th order method

6.3.7 Example with Runga Kutta 4 th order method 6.3.7 Example wth Runga Kutta 4 th order method Agan, as an example, 3 machne, 9 bus system shown n Fg. 6.4 s agan consdered. Intally, the dampng of the generators are neglected (.e. d = 0 for = 1, 2,

More information

Computation of Higher Order Moments from Two Multinomial Overdispersion Likelihood Models

Computation of Higher Order Moments from Two Multinomial Overdispersion Likelihood Models Computaton of Hgher Order Moments from Two Multnomal Overdsperson Lkelhood Models BY J. T. NEWCOMER, N. K. NEERCHAL Department of Mathematcs and Statstcs, Unversty of Maryland, Baltmore County, Baltmore,

More information

Parameters Estimation of the Modified Weibull Distribution Based on Type I Censored Samples

Parameters Estimation of the Modified Weibull Distribution Based on Type I Censored Samples Appled Mathematcal Scences, Vol. 5, 011, no. 59, 899-917 Parameters Estmaton of the Modfed Webull Dstrbuton Based on Type I Censored Samples Soufane Gasm École Supereure des Scences et Technques de Tuns

More information

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method Soluton of Lnear System of Equatons and Matr Inverson Gauss Sedel Iteraton Method It s another well-known teratve method for solvng a system of lnear equatons of the form a + a22 + + ann = b a2 + a222

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

Improved Approximation Methods to the Stopped Sum Distribution

Improved Approximation Methods to the Stopped Sum Distribution Internatonal Journal of Mathematcal Analyss and Applcatons 2017; 4(6): 42-46 http://www.aasct.org/journal/jmaa ISSN: 2375-3927 Improved Approxmaton Methods to the Stopped Sum Dstrbuton Aman Al Rashd *,

More information

Iterative General Dynamic Model for Serial-Link Manipulators

Iterative General Dynamic Model for Serial-Link Manipulators EEL6667: Knematcs, Dynamcs and Control of Robot Manpulators 1. Introducton Iteratve General Dynamc Model for Seral-Lnk Manpulators In ths set of notes, we are gong to develop a method for computng a general

More information

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH Turbulence classfcaton of load data by the frequency and severty of wnd gusts Introducton Oscar Moñux, DEWI GmbH Kevn Blebler, DEWI GmbH Durng the wnd turbne developng process, one of the most mportant

More information

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics

ECONOMICS 351*-A Mid-Term Exam -- Fall Term 2000 Page 1 of 13 pages. QUEEN'S UNIVERSITY AT KINGSTON Department of Economics ECOOMICS 35*-A Md-Term Exam -- Fall Term 000 Page of 3 pages QUEE'S UIVERSITY AT KIGSTO Department of Economcs ECOOMICS 35* - Secton A Introductory Econometrcs Fall Term 000 MID-TERM EAM ASWERS MG Abbott

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Exerments-I MODULE III LECTURE - 2 EXPERIMENTAL DESIGN MODELS Dr. Shalabh Deartment of Mathematcs and Statstcs Indan Insttute of Technology Kanur 2 We consder the models

More information

STAT 511 FINAL EXAM NAME Spring 2001

STAT 511 FINAL EXAM NAME Spring 2001 STAT 5 FINAL EXAM NAME Sprng Instructons: Ths s a closed book exam. No notes or books are allowed. ou may use a calculator but you are not allowed to store notes or formulas n the calculator. Please wrte

More information

Chapter 13: Multiple Regression

Chapter 13: Multiple Regression Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to

More information

STATIC ANALYSIS OF TWO-LAYERED PIEZOELECTRIC BEAMS WITH IMPERFECT SHEAR CONNECTION

STATIC ANALYSIS OF TWO-LAYERED PIEZOELECTRIC BEAMS WITH IMPERFECT SHEAR CONNECTION STATIC ANALYSIS OF TWO-LERED PIEZOELECTRIC BEAMS WITH IMPERFECT SHEAR CONNECTION Ákos József Lengyel István Ecsed Assstant Lecturer Emertus Professor Insttute of Appled Mechancs Unversty of Mskolc Mskolc-Egyetemváros

More information

RELIABILITY ASSESSMENT

RELIABILITY ASSESSMENT CHAPTER Rsk Analyss n Engneerng and Economcs RELIABILITY ASSESSMENT A. J. Clark School of Engneerng Department of Cvl and Envronmental Engneerng 4a CHAPMAN HALL/CRC Rsk Analyss for Engneerng Department

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Supplementary material: Margin based PU Learning. Matrix Concentration Inequalities

Supplementary material: Margin based PU Learning. Matrix Concentration Inequalities Supplementary materal: Margn based PU Learnng We gve the complete proofs of Theorem and n Secton We frst ntroduce the well-known concentraton nequalty, so the covarance estmator can be bounded Then we

More information

P R. Lecture 4. Theory and Applications of Pattern Recognition. Dept. of Electrical and Computer Engineering /

P R. Lecture 4. Theory and Applications of Pattern Recognition. Dept. of Electrical and Computer Engineering / Theory and Applcatons of Pattern Recognton 003, Rob Polkar, Rowan Unversty, Glassboro, NJ Lecture 4 Bayes Classfcaton Rule Dept. of Electrcal and Computer Engneerng 0909.40.0 / 0909.504.04 Theory & Applcatons

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

Randić Energy and Randić Estrada Index of a Graph

Randić Energy and Randić Estrada Index of a Graph EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS Vol. 5, No., 202, 88-96 ISSN 307-5543 www.ejpam.com SPECIAL ISSUE FOR THE INTERNATIONAL CONFERENCE ON APPLIED ANALYSIS AND ALGEBRA 29 JUNE -02JULY 20, ISTANBUL

More information

Lecture 3. Ax x i a i. i i

Lecture 3. Ax x i a i. i i 18.409 The Behavor of Algorthms n Practce 2/14/2 Lecturer: Dan Spelman Lecture 3 Scrbe: Arvnd Sankar 1 Largest sngular value In order to bound the condton number, we need an upper bound on the largest

More information

2.3 Nilpotent endomorphisms

2.3 Nilpotent endomorphisms s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms

More information

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 9: Statistical Inference and the Relationship between Two Variables Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,

More information

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP C O L L O Q U I U M M A T H E M A T I C U M VOL. 80 1999 NO. 1 FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP BY FLORIAN K A I N R A T H (GRAZ) Abstract. Let H be a Krull monod wth nfnte class

More information

The Jacobsthal and Jacobsthal-Lucas Numbers via Square Roots of Matrices

The Jacobsthal and Jacobsthal-Lucas Numbers via Square Roots of Matrices Internatonal Mathematcal Forum, Vol 11, 2016, no 11, 513-520 HIKARI Ltd, wwwm-hkarcom http://dxdoorg/1012988/mf20166442 The Jacobsthal and Jacobsthal-Lucas Numbers va Square Roots of Matrces Saadet Arslan

More information

arxiv: v1 [math.co] 12 Sep 2014

arxiv: v1 [math.co] 12 Sep 2014 arxv:1409.3707v1 [math.co] 12 Sep 2014 On the bnomal sums of Horadam sequence Nazmye Ylmaz and Necat Taskara Department of Mathematcs, Scence Faculty, Selcuk Unversty, 42075, Campus, Konya, Turkey March

More information

Linear Regression Analysis: Terminology and Notation

Linear Regression Analysis: Terminology and Notation ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented

More information

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence

More information

An Improved multiple fractal algorithm

An Improved multiple fractal algorithm Advanced Scence and Technology Letters Vol.31 (MulGraB 213), pp.184-188 http://dx.do.org/1.1427/astl.213.31.41 An Improved multple fractal algorthm Yun Ln, Xaochu Xu, Jnfeng Pang College of Informaton

More information

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011 Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected

More information

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications Durban Watson for Testng the Lack-of-Ft of Polynomal Regresson Models wthout Replcatons Ruba A. Alyaf, Maha A. Omar, Abdullah A. Al-Shha ralyaf@ksu.edu.sa, maomar@ksu.edu.sa, aalshha@ksu.edu.sa Department

More information

Continuous Time Markov Chain

Continuous Time Markov Chain Contnuous Tme Markov Chan Hu Jn Department of Electroncs and Communcaton Engneerng Hanyang Unversty ERICA Campus Contents Contnuous tme Markov Chan (CTMC) Propertes of sojourn tme Relatons Transton probablty

More information

EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES

EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES Manuel J. C. Mnhoto Polytechnc Insttute of Bragança, Bragança, Portugal E-mal: mnhoto@pb.pt Paulo A. A. Perera and Jorge

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

LINEAR REGRESSION ANALYSIS. MODULE VIII Lecture Indicator Variables

LINEAR REGRESSION ANALYSIS. MODULE VIII Lecture Indicator Variables LINEAR REGRESSION ANALYSIS MODULE VIII Lecture - 7 Indcator Varables Dr. Shalabh Department of Maematcs and Statstcs Indan Insttute of Technology Kanpur Indcator varables versus quanttatve explanatory

More information

Thermodynamics and statistical mechanics in materials modelling II

Thermodynamics and statistical mechanics in materials modelling II Course MP3 Lecture 8/11/006 (JAE) Course MP3 Lecture 8/11/006 Thermodynamcs and statstcal mechancs n materals modellng II A bref résumé of the physcal concepts used n materals modellng Dr James Ellott.1

More information

The Study of Teaching-learning-based Optimization Algorithm

The Study of Teaching-learning-based Optimization Algorithm Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute

More information

DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM

DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM Ganj, Z. Z., et al.: Determnaton of Temperature Dstrbuton for S111 DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM by Davood Domr GANJI

More information

Non-Mixture Cure Model for Interval Censored Data: Simulation Study ABSTRACT

Non-Mixture Cure Model for Interval Censored Data: Simulation Study ABSTRACT Malaysan Journal of Mathematcal Scences 8(S): 37-44 (2014) Specal Issue: Internatonal Conference on Mathematcal Scences and Statstcs 2013 (ICMSS2013) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Journal

More information

Chapter 8 Indicator Variables

Chapter 8 Indicator Variables Chapter 8 Indcator Varables In general, e explanatory varables n any regresson analyss are assumed to be quanttatve n nature. For example, e varables lke temperature, dstance, age etc. are quanttatve n

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

Case Study of Markov Chains Ray-Knight Compactification

Case Study of Markov Chains Ray-Knight Compactification Internatonal Journal of Contemporary Mathematcal Scences Vol. 9, 24, no. 6, 753-76 HIKAI Ltd, www.m-har.com http://dx.do.org/.2988/cms.24.46 Case Study of Marov Chans ay-knght Compactfcaton HaXa Du and

More information

POWER AND SIZE OF NORMAL DISTRIBUTION AND ITS APPLICATIONS

POWER AND SIZE OF NORMAL DISTRIBUTION AND ITS APPLICATIONS Jurnal Ilmah Matematka dan Penddkan Matematka (JMP) Vol. 9 No., Desember 07, hal. -6 ISSN (Cetak) : 085-456; ISSN (Onlne) : 550-04; https://jmpunsoed.com/ POWER AND SIZE OF NORMAL DISTRIBION AND ITS APPLICATIONS

More information

Fluctuation Results For Quadratic Continuous-State Branching Process

Fluctuation Results For Quadratic Continuous-State Branching Process IOSR Journal of Mathematcs (IOSR-JM) e-issn: 2278-5728, p-issn: 2319-765X. Volume 13, Issue 3 Ver. III (May - June 2017), PP 54-61 www.osrjournals.org Fluctuaton Results For Quadratc Contnuous-State Branchng

More information

The Geometry of Logit and Probit

The Geometry of Logit and Probit The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

ISSN X Robust bayesian inference of generalized Pareto distribution

ISSN X Robust bayesian inference of generalized Pareto distribution Afrka Statstka Vol. 112), 2016, pages 1061 1074. DOI: http://dx.do.org/10.16929/as/2016.1061.92 Afrka Statstka ISSN 2316-090X Robust bayesan nference of generalzed Pareto dstrbuton Fatha Mokran 1, Hocne

More information

Analysis of the Magnetomotive Force of a Three-Phase Winding with Concentrated Coils and Different Symmetry Features

Analysis of the Magnetomotive Force of a Three-Phase Winding with Concentrated Coils and Different Symmetry Features Analyss of the Magnetomotve Force of a Three-Phase Wndng wth Concentrated Cols and Dfferent Symmetry Features Deter Gerlng Unversty of Federal Defense Munch, Neubberg, 85579, Germany Emal: Deter.Gerlng@unbw.de

More information

CALCULUS CLASSROOM CAPSULES

CALCULUS CLASSROOM CAPSULES CALCULUS CLASSROOM CAPSULES SESSION S86 Dr. Sham Alfred Rartan Valley Communty College salfred@rartanval.edu 38th AMATYC Annual Conference Jacksonvlle, Florda November 8-, 202 2 Calculus Classroom Capsules

More information

Statistics Chapter 4

Statistics Chapter 4 Statstcs Chapter 4 "There are three knds of les: les, damned les, and statstcs." Benjamn Dsrael, 1895 (Brtsh statesman) Gaussan Dstrbuton, 4-1 If a measurement s repeated many tmes a statstcal treatment

More information

ANOMALIES OF THE MAGNITUDE OF THE BIAS OF THE MAXIMUM LIKELIHOOD ESTIMATOR OF THE REGRESSION SLOPE

ANOMALIES OF THE MAGNITUDE OF THE BIAS OF THE MAXIMUM LIKELIHOOD ESTIMATOR OF THE REGRESSION SLOPE P a g e ANOMALIES OF THE MAGNITUDE OF THE BIAS OF THE MAXIMUM LIKELIHOOD ESTIMATOR OF THE REGRESSION SLOPE Darmud O Drscoll ¹, Donald E. Ramrez ² ¹ Head of Department of Mathematcs and Computer Studes

More information

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

Statistical Evaluation of WATFLOOD

Statistical Evaluation of WATFLOOD tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth

More information

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD Ákos Jósef Lengyel, István Ecsed Assstant Lecturer, Professor of Mechancs, Insttute of Appled Mechancs, Unversty of Mskolc, Mskolc-Egyetemváros,

More information

Lecture 17 : Stochastic Processes II

Lecture 17 : Stochastic Processes II : Stochastc Processes II 1 Contnuous-tme stochastc process So far we have studed dscrete-tme stochastc processes. We studed the concept of Makov chans and martngales, tme seres analyss, and regresson analyss

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 31 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 6. Rdge regresson The OLSE s the best lnear unbased

More information

FUZZY FINITE ELEMENT METHOD

FUZZY FINITE ELEMENT METHOD FUZZY FINITE ELEMENT METHOD RELIABILITY TRUCTURE ANALYI UING PROBABILITY 3.. Maxmum Normal tress Internal force s the shear force, V has a magntude equal to the load P and bendng moment, M. Bendng moments

More information

THE VIBRATIONS OF MOLECULES II THE CARBON DIOXIDE MOLECULE Student Instructions

THE VIBRATIONS OF MOLECULES II THE CARBON DIOXIDE MOLECULE Student Instructions THE VIBRATIONS OF MOLECULES II THE CARBON DIOXIDE MOLECULE Student Instructons by George Hardgrove Chemstry Department St. Olaf College Northfeld, MN 55057 hardgrov@lars.acc.stolaf.edu Copyrght George

More information

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 13 The Smple Lnear Regresson Model and Correlaton 1999 Prentce-Hall, Inc. Chap. 13-1 Chapter Topcs Types of Regresson Models Determnng the Smple Lnear

More information

A CHARACTERIZATION OF ADDITIVE DERIVATIONS ON VON NEUMANN ALGEBRAS

A CHARACTERIZATION OF ADDITIVE DERIVATIONS ON VON NEUMANN ALGEBRAS Journal of Mathematcal Scences: Advances and Applcatons Volume 25, 2014, Pages 1-12 A CHARACTERIZATION OF ADDITIVE DERIVATIONS ON VON NEUMANN ALGEBRAS JIA JI, WEN ZHANG and XIAOFEI QI Department of Mathematcs

More information

Finding Dense Subgraphs in G(n, 1/2)

Finding Dense Subgraphs in G(n, 1/2) Fndng Dense Subgraphs n Gn, 1/ Atsh Das Sarma 1, Amt Deshpande, and Rav Kannan 1 Georga Insttute of Technology,atsh@cc.gatech.edu Mcrosoft Research-Bangalore,amtdesh,annan@mcrosoft.com Abstract. Fndng

More information

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9 Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,

More information

Pop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing

Pop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing Advanced Scence and Technology Letters, pp.164-168 http://dx.do.org/10.14257/astl.2013 Pop-Clc Nose Detecton Usng Inter-Frame Correlaton for Improved Portable Audtory Sensng Dong Yun Lee, Kwang Myung Jeon,

More information

Appendix for Causal Interaction in Factorial Experiments: Application to Conjoint Analysis

Appendix for Causal Interaction in Factorial Experiments: Application to Conjoint Analysis A Appendx for Causal Interacton n Factoral Experments: Applcaton to Conjont Analyss Mathematcal Appendx: Proofs of Theorems A. Lemmas Below, we descrbe all the lemmas, whch are used to prove the man theorems

More information

The lower and upper bounds on Perron root of nonnegative irreducible matrices

The lower and upper bounds on Perron root of nonnegative irreducible matrices Journal of Computatonal Appled Mathematcs 217 (2008) 259 267 wwwelsevercom/locate/cam The lower upper bounds on Perron root of nonnegatve rreducble matrces Guang-Xn Huang a,, Feng Yn b,keguo a a College

More information

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models ECO 452 -- OE 4: Probt and Logt Models ECO 452 -- OE 4 Maxmum Lkelhood Estmaton of Bnary Dependent Varables Models: Probt and Logt hs note demonstrates how to formulate bnary dependent varables models

More information