New Entropy Estimators with Smaller Root Mean Squared Error
|
|
- Brendan Underwood
- 6 years ago
- Views:
Transcription
1 Joural of Moder Applied Statistical Methods Volume 4 Issue 2 Article New Etropy Estimators with Smaller Root Mea Squared Error Amer Ibrahim Al-Omari Al al-bayt Uiversity, Mafraq, Jorda, alomari_amer@yahoo.com Follow this ad additioal works at: Part of the Applied Statistics Commos, Social ad Behavioral Scieces Commos, ad the Statistical Theory Commos Recommeded Citatio Al-Omari, Amer Ibrahim (205) "New Etropy Estimators with Smaller Root Mea Squared Error," Joural of Moder Applied Statistical Methods: Vol. 4 : Iss. 2, Article 0. DOI: /jmasm/ Available at: This Regular Article is brought to you for free ad ope access by the Ope Access Jourals at DigitalCommos@WayeState. It has bee accepted for iclusio i Joural of Moder Applied Statistical Methods by a authorized editor of DigitalCommos@WayeState.
2 New Etropy Estimators with Smaller Root Mea Squared Error Cover Page Footote The author thaks the editor ad the referees for their helpful ad valuable commets that substatially improved this paper. This regular article is available i Joural of Moder Applied Statistical Methods: iss2/0
3 Joural of Moder Applied Statistical Methods November 205, Vol. 4, No. 2, Copyright 205 JMASM, Ic. ISSN New Etropy Estimators with Smaller Root Mea Squared Error Amer Ibrahim Al-Omari Al al-bayt Uiversity Mafraq, Jorda New estimators of etropy of cotiuous radom variable are suggested. The proposed estimators are ivestigated uder simple radom samplig (SRS), raked set samplig (RSS), ad double raked set samplig (DRSS) methods. The estimators are compared with Vasicek (976) ad Al-Omari (204) etropy estimators theoretically ad by simulatio i terms of the root mea squared error (RMSE) ad bias values. The results idicate that the suggested estimators have less RMSE ad bias values tha their competig estimators itroduced by Vasicek (976) ad Al-Omari (204). Keywords: Shao etropy; simple radom samplig, raked set samplig; double raked set samplig; root mea square error. Itroductio The raked set samplig was first suggested by McItyre (952) to estimate a mea of pasture ad forage yields. It is a cost efficiet samplig procedure alterative to the commoly used simple radom samplig scheme. The RSS is useful i situatios where the visual orderig of a set of uits ca be doe easily, but the exact measuremet of the uits is difficult or expesive. Let the variable of iterest X has a probability desity fuctio (pdf) g(x) ad a cumulative distributio fuctio (cdf) G(x), with mea μ ad variace σ 2. Let g (i:) (x) ad G (i:) (x) be the pdf ad cdf of the ith order statistic, X (i:), ( i ) of a radom sample of size. The pdf ad the cdf of X (i:), respectively, are give by i i g( i : ) x G x Gx g x, x i Amer Ibrahim Al-Omari is Faculty of Sciece, i the Departmet of Mathematics. at: alomari_amer@yahoo.com. 88
4 AMER IBRAHIM AL-OMARI ad j j G : x G x G x, x i j j, 2 i: x dx ad variace i: i: x i : g x dx i:. The raked set samplig method ca be describes as follows: with mea x g 2 Step. Radomly select 2 uits from the target populatio. Step 2. Allocate the 2 selected uits radomly ito sets, each of size. Step 3. Without yet kowig ay values for the variable of iterest, rak the uits withi each set with respect to a variable of iterest. This may be based o a persoal professioal judgmet or based o a cocomitat variable correlated with the variable of iterest. Step 4. The sample uits are selected for actual measuremet by icludig the ith smallest raked uit of the ith sample (i =, 2,, ). Step 5. Repeat Steps through 4 for r cycles to obtai a sample of size r for actual measuremet. It is of iterest to ote here that eve if 2 uits are selected from the populatio, but oly of them are measured for compariso with a simple radom samplig of the same size. Let the measured RSS uits are deoted by X (:), X 2(2:),, X (:). The i RSS estimator of the populatio mea is defied as XRSS Xii :. Takahasi ad Wakimoto (968) provided the mathematical theory of the RSS ad showed that 2 i: i: RSS 2 ii: g x g x,, Var X. i i i Al-Saleh ad Al-Kadiri (2000) suggested double raked set samplig (DRSS) method for estimatig the populatio mea to icrease the efficiecy of the estimators for fixed sample size. The DRSS method ca be described as: 2 89
5 NEW ENTROPY ESTIMATORS Step. Radomly choose 2 samples of size each from the target populatio. Step 2. Apply the RSS method described above o the 2 samples i Step. This step yields samples of size each. Step 3. Reapply the RSS method agai o the samples obtaied i Step 2 to obtai a sample of size from the DRSS data. The cycle ca be repeated r times if eeded to obtai a sample of size r uits. Let X be a cotiuous radom variable with probability desity fuctio gx ( ) ad cumulative distributio fuctio G(x). The etropy H [g(x)] of the radom variable is defied by Shao (948a, 948b) as H g x g x log g x dx. () The problem of etropy estimatio of a cotiuous radom variable is cosidered by may authors. Vasicek's (976) suggested a estimator of etropy based o spacig's as log dg p H g x dp, 0 dp (2) where the estimatio is foud by replacig the distributio fuctio G(x) by the empirical distributio fuctio G (x), ad usig the differece operator istead of d the differetial operator. The the derivative G p is estimated by a dp fuctio of the order statistics. Let X, X 2,, X be a simple radom sample of size from G(x) ad X () < X (2) < < X () be the order statistics of the sample. The Vasicek's (976) estimator of H [g(x)] is defied as HV log X X 2 (3) m i m i m i m 90
6 AMER IBRAHIM AL-OMARI where m < / 2 is a positive iteger kow as the widow size, X (i - m) = X () if P. i m, ad X (i + m) = X () if i m. He proved that HVm H g x as m, m, ad 0. Va Es (992) suggested a estimator of etropy based o spacigs as m HVEm X X i m i log m log m i m km k (4) ad proved the cosistecy ad the asymptotic ormality of the estimator uder some coditios. Ebrahimi, Pflughoeft, ad Soofi (994) adjusted the weights of Vasicek (976) estimator to have a smaller weights ad proposed a etropy estimator give by where HE X X log (5) m i m i m i im i, i m, m i 2, m i m, i, m i, m where X (i-m) = X () for i m ad X (i+m) = X () for i m. Ebrahimi et al. (994) showed by simulatio that their estimator has a smaller bias ad mea squared error tha Vasicek (976) estimator. Also, they proved that P. HEm H g x m m as,, 0. Noughabi ad Noughabi (203) suggested a ew estimator of etropy of a ukow cotiuous probability desity fuctio as 9
7 NEW ENTROPY ESTIMATORS HNN log s, m, (6) m i i where ad gˆ X i gˆ X, i m, i 2 m/ si, m, m i m, Xi m X im gˆ X, m i, i Xi X j k, where h is badwidth ad k is a kerel fuctio h h j satisfies k xdx. They proved that HNN P m H g x as, m, m / 0. Note that the kerel fuctio i Noughabi ad Noughabi (203) is selected to be the stadard ormal distributio ad the badwidth h is chose to be h =.06s -/5, where s is the sample stadard deviatio. To estimate the etropy H [g(x)] of a ukow cotiuous probability desity fuctio g(x), Noughabi ad Arghami (200) suggested a etropy estimator give by where HN X X log (7) m i m i m i cim, im, ci 2, m i m,, m i, ad X (i-m) = X () if i m ad X (i+m) = X () for i m. Correa (995) suggested a modified etropy estimator to have smaller mea squared error i the form 92
8 AMER IBRAHIM AL-OMARI HC m i im j i X X j i j i m log, im X X 2 j i j i m (8) im where X X i j. 2m j i m Al-Omari (204) suggested three estimators of etropy of a ukow cotiuous probability desity fuctio g(x) usig SRS, RSS, ad DRSS methods. Based o SRS his first suggested estimator is defied as AHESRS X X log (9) m i m i m i im where X (i-m) = X () for i m, X (i+m) = X () for i m, ad, i m, 2 i 2, m i m,, m i, 2 (0) The secod ad third estimators suggested by Al-Omari (204), based o RSS ad DRSS respectively, are give by ad AHERSS X X * * log () m i m i m i im AHEDRSS X X ** ** log (2) m i m i m i im * * * * where Xi m X () for i m ad X X im ( ) for i m, ad 93
9 NEW ENTROPY ESTIMATORS X X ** ** X im () for i m ad i m X for i m. ** ** ( ) For more about etropy estimators, see Choi, Kim, ad Sog (2004), Park, Park (2003), Goria, Leoeko, Mergel, ad Novi Iverardi (2005) ad Choi (2008). The remaiig part of this paper is orgaized as follows. The suggested etropy estimators are give i the sectio, Proposed Estimators. Next, a simulatio study is coducted to compare the ew estimators with their couterparts suggested by Vasicek (976) ad Al-Omari (204). Fially, some coclusios ad suggestios for further works. The proposed estimators The coefficiet of the etropy estimators i Ebrahimi et al. (994), Noughabi ad (0) (0) (0) Arghami (200), ad Al-Omari (204) are adjusted. Let X, X 2,..., X be a simple radom sample of size from G (x). Based o SRS the first suggested estimator is give by where SHESRS X X 0 0 log (3) m i m i m i im, i m, 4 i 2, m i m,, m i, 4 (4) (0) (0) (0) (0) Xi m X () for i m ad X X im ( ) for i m. Comparig (3) with (3), we have 94
10 AMER IBRAHIM AL-OMARI Let 0 0 SHESRSm log X i m X im i im 2 HVSRSm log (5) () () () (: ) (2: ) ( : ) HVSRS m i 2m 8 log 5 X, X,..., X be a RSS of size, Vasicek (976) etropy estimator usig RSS as cosidered by Mahdizadeh (202) is give by HVRSS log X X 2 i (6) m i m i m i m Based o the RSS uits etropy estimator is X (), (: ) X (), (2: ), X () ( : ), the secod suggested SHERSS X X log (7) m i m i m i im () () () () where i is defied as i (4), ad Xi m X () for i m ad X X im ( ) for i m. Comparig (6) with (7) to have Assume that () () SHERSSm log X i m X im i im 2 HVRSSm Log (8) HVRSS m i 2m 8 log 5 X, X,..., X is a DRSS sample of size. The third (2) (2) (2) (: ) (2: ) ( : ) suggested etropy estimator has the form i 95
11 NEW ENTROPY ESTIMATORS SHEDRSS X X 2 2 log (9) m i m i m i im (2) (2) (2) (2) where i is defied as i (4), ad Xi m X () for i m ad X X im ( ) for i m. Based o DRSS method Mahdizadeh (202) showed that Vasicek (976) estimator will be SHEDRSS log X X (20) m i m i m i m Comparig (9) with (20) to get 2 2 SHEDRSSm log X i m X im i im 2 HVDRSSm log (2) HVDRSS ME Remark : The etropy m i 2m 8 log 5 H f of a empirical maximum etropy desity ME f which is related to HVSRS ad SHESRS ca be computed followig Theil (980) as: i ME 2 2log 2 H f HVSRS log 2 SHESRS log SHESRS log 5 (22) ME Remark 2: If i (22), the H f SHESRS. I the followig two theorems, we compared the suggested estimators with Vasicek (967) ad Al-Omari (204). 96
12 AMER IBRAHIM AL-OMARI Theorem : The suggested estimators have the followig properties: (0) (0) (0) a) Let X, X 2,..., X be SRS of size, the SHESRS m > HVSRS m. () () () b) Let X (), X (2),..., X ( ) be a RSS of size, the SHERSS m > HVRSS m. (2) (2) (2) c) Let X (), X (2),..., X ( ) be a DRSS of size, the SHEDRSS m > HVDRSS m. Proof: The proof of (a), (b), (c), is straightforward by usig (5), (8), (2), respectively, where 2 m log I the followig theorem, we compare our suggested etropy estimators with their competitors i Al-Omari (204). Theorem 2: Based o the suggested estimators ad Al-Omari (204) etropy respectively, we have SHEj m > AHEj m, j = SRS, RSS, DRSS. Proof: Compare (9) with (3) based o SRS to obtai SHESRS m 2m 6 AHESRS m log, 5 ad sice 2 m log 6 0, the the case of SRS holds. Also, compare () with (7) 5 based o RSS, ad (2) with (9) usig DRSS to complete the proof of this theorem. The followig theorem proves the cosistecy of the suggested estimators SHESRS m, SHERSS m, ad SHEDRSS m. Theorem 3: Let Ω be the class of cotiuous desities with fiite etropies ad let X, X 2,, X be a radom sample from g Ω. If, m, m/ 0, the SHEj m, (j = SRS, RSS, DRSS) coverges i probability to H [g(x)]. Proof: Based o the simple radom samplig, from (5) we have 97
13 NEW ENTROPY ESTIMATORS SHESRS m 2m 8 HVSRS m log, 5 ad Vasicek (976) showed that HVSRS m coverges i probability to H [g(x)] ad sice 2 m log 8 coverges to zero as goes to ifiity, the we proved the 5 case of the SRS. Follow the same approach ad use (8) ad (2) to prove the theorem for RSS ad DRSS estimators, respectively. Methodology Simulatio study A simulatio was coducted to ivestigate the performace of the suggested etropy estimators with Vasicek (976) ad Al-Omari (204) etropy estimators usig samplig methods cosidered i this study. The compariso is based o the root mea squared errors (RMSEs) ad bias values of the estimators for 0000 samples geerated from the uiform, expoetial ad the stadard ormal distributios usig SRS, RSS ad DRSS methods. The selectio of the optimal values of the widow size of m for a give value is as yet a ope problem i the etropy estimatio. Therefore, we used the heuristic formula m 0.5 suggested by Wieczorkowski ad Grzegorzewski (999) to select m ad to compute the RMSEs of etropy estimators. I this study, we cosidered the sample ad widow sizes as give i Table. Table. The sample ad widow sizes cosidered i this simulatio Sample size = 0 = 20 = 30 Widow size m 5 m 0 m 5 Also, the performace of the RMSE of the suggested estimators for samples geerated from the uiform, expoetial ad stadard ormal distributios is evaluated based o the quatity Q N HVjm N 00, N SHEj m, AHEj m, j SRS, RSS, DRSS. HVj m 98
14 AMER IBRAHIM AL-OMARI The results are summarized i Tables 2-6. Also, we compared the suggested estimators of etropy with their competitors suggested by Al-Omari (204) ad the results preseted i Table 7 are take from Al-Omari (204). Based o these results observe the followig. The suggested etropy estimators usig SRS, RSS ad DRSS methods are more efficiet tha their competitors HV m based o the same method for all cases cosidered i this study. As a example, from Table 3, with = 0 ad m = 3 for the expoetial distributio with H [g(x)] = usig RSS method, the RMSE ad bias value of SHERSS m are ad compared to ad the RMSE ad bias of HVRSS m. The SHEDRSS m is superior to the other suggested estimators, SHERSS m ad SHESRS m uder the uiform, expoetial ad ormal distributios. From Table, cosider the case of = 20 ad m = 4 uder the uiform distributio whe H [g(x)] = 0, it ca be oted that the RMSE values of SHEDRSS m, SHERSS m, ad SHESRS m are , ad , respectively. The ature of the uderlyig distributio as well as the value of H [g(x)] affect o the efficiecy of the estimator usig the same method. As a example, the Q values with = 30 ad m = 3 SHERSS m for the uiform, expoetial, ad the stadard ormal distributios are , ad , respectively. However, the values of Q for the uiform distributio with H [g(x)] = 0 are SHE m superior to their couterparts for the expoetial ad ormal distributios. Fially, the suggested etropy estimators are foud to be more efficiet tha their competitors i Al-Omari (204) etropy estimators usig SRS, RSS ad DRSS schemes for the same widow ad sample sizes. For illustratio, assume that = 30 ad m = 8 whe the uderlyig distributio is the stadard ormal, from Table 4, the RMSE of SHERSS m is compared to which is the RMSE of AHERSS m as show i Table 7. 99
15 NEW ENTROPY ESTIMATORS Table 2. The Mote Carlo RMSEs ad bias values of HV m ad SHE m for the uiform distributio with H [g(x)] = 0. SRS HV m SHE m RSS Q SHE m HV m SHE m m Bias RMSE Bias RMSE Bias RMSE Bias RMSE Q SHE m Table 2 cotiued o ext page 00
16 AMER IBRAHIM AL-OMARI Table 3. The Mote Carlo RMSEs ad bias values of HV m ad SHE m for the expoetial distributio with H [g(x)] =. SRS HV m SHE m RSS Q SHE m HV m SHE m m Bias RMSE Bias RMSE Bias RMSE Bias RMSE Q SHE m Table 3 cotiued o ext page 0
17 NEW ENTROPY ESTIMATORS Table 4. The Mote Carlo RMSEs ad bias values of HV m ad SHE m for the stadard ormal distributio ad H [g(x)] =.49. SRS HV m SHE m RSS Q SHE m HV m SHE m m Bias RMSE Bias RMSE Bias RMSE Bias RMSE Q SHE m Table 4 cotiued o ext page 02
18 AMER IBRAHIM AL-OMARI Table 5. The Mote Carlo RMSEs ad bias values of HV m ad SHE m for the uiform distributio with H [g(x)] = 0 ad expoetial distributio with H [g(x)] = usig DRSS. SRS HV m m SHE SHEm RSS Q HV m m SHE Q SHEm m Bias RMSE Bias RMSE Bias RMSE Bias RMSE Table 5 cotiued o ext page 03
19 NEW ENTROPY ESTIMATORS
20 AMER IBRAHIM AL-OMARI Table 6. The Mote Carlo RMSEs ad bias values of HV m ad SHE m for the stadard ormal distributio ad H [g(x)] =.49. HV m SHE m m Bias RMSE Bias RMSE Q SHE m
21 NEW ENTROPY ESTIMATORS Table 7. The Mote Carlo RMSEs ad bias values of AHEj m, j = SRS, RSS, DRSS (Al-Omari, 204). AHESRS m Q AHESRS AHERSS m Q AHERSS AHEDRSS m Q AHEDRSS m Bias RMSE Bias RMSE Bias RMSE Uiform distributio with H [g(x)] = Expoetial distributio with H [g(x)] = Stadard ormal distributio with H [g(x)] =
22 AMER IBRAHIM AL-OMARI Coclusio Three etropy estimators are suggested usig SRS, RSS, ad DRSS methods. The cosistecy of these estimators is proved as well as some properties are reported. Based o theoretical ad umerical comparisos the suggested etropy estimators are more efficiet tha Vasicek (976) ad Al-Omari (204) etropy estimators. However, the suggested estimators of etropy i this paper ca be exteded by cosiderig other samplig methods such as the multistage RSS ad media RSS methods. Ackowledgemets The author thaks the referees for their helpful ad valuable commets that substatially improved this paper. Refereces Al-Omari, A. I. (204). Estimatio of etropy usig radom samplig. Joural of Computatio ad Applied Mathematics, 26, doi:0.06/j.cam Al-Saleh, M. F. & Al-Kadiri, M. A. (2000). Double raked set samplig. Statistics ad Probability Letters, 48(2), doi:0.06/s (99) Choi, B. (2008). Improvemet of goodess of fit test for ormal distributio based o etropy ad power compariso. Joural of Statistical Computatio ad Simulatio, 78(9), doi:0.080/ Choi, B., Kim, K., & Sog, S. H. (2004). Goodess of fit test for expoetiality based o Kullback-Leibler iformatio. Commuicatio i Statistics-Simulatio ad Computatio, 33(2), doi:0.08/sac Goria, M. N., Leoeko, N. N., Mergel, V. V., & Novi Iverardi, P. L. (2005). A ew class of radom vector etropy estimators ad its applicatios i testig statistical hypotheses. Joural of Noparametric Statistics, 7(3), doi:0.080/
23 NEW ENTROPY ESTIMATORS Correa, J. C. (995). A ew estimator of etropy. Commuicatio i Statistics-Theory Methods, 24(0), doi:0.080/ Ebrahimi, N., Pflughoeft, K., & Soofi, E. S. (994). Two measures of sample etropy. Statistics & Probability Letters, 20(3), doi:0.06/ (94) Mahdizadeh, M. (202). O the use of raked set samples i etropy based test of fit for the Laplace distributio. Revista Colombiaa de Estadística, 35(3), McItyre, G. A. (952). A method for ubiased selective samplig usig raked sets. Australia Joural of Agricultural Research, 3(4), doi:0.07/ar Noughabi, H. A. & Noughabi, R. A. (203). O the etropy estimators. Joural of Statistical Computatio ad Simulatio, 83(4), doi:0.080/ Noughabi, H. A. & Arghami, N. R. (200). A ew estimator of etropy. Joural of the Iraia Statistical Society, 9(), Park, S. & Park, D. (2003). Correctig momets for goodess of fit tests based o two etropy estimates. Joural of Statistical Computatio ad Simulatio, 73(9), doi:0.080/ Shao, C. E. (948a). A mathematical theory of commuicatios. Bell System Techical Joural 27(3), doi:0.002/j tb0338.x Shao, C. E. (948b). A mathematical theory of commuicatios. Bell System Techical Joural 27(4), doi:0.002/j tb0097.x Takahasi, K. & Wakimoto, K. (968). O the ubiased estimates of the populatio mea based o the sample stratified by meas of orderig. Aals of the Istitute of Statistical Mathematics, 20(), -3. doi:0.007/bf Theil, J. (980). The etropy of maximum etropy distributio. Ecoomics Letters, 5(2), doi:0.06/ (80) Va Es, B. (992). Estimatig fuctioals related to a desity by class of statistics based o spacigs. Scadiavia Joural of Statistics, 9(), Vasicek, O. (976). A test for ormality based o sample etropy. Joural of the Royal Statistical Society, B, 38,
24 AMER IBRAHIM AL-OMARI Wieczorkowski, R. & Grzegorzewsky, P. (999). Etropy estimators - improvemets ad comparisos. Commuicatio i Statistics-Simulatio ad Computatio, 28(2), doi:0.080/
Estimation of Gumbel Parameters under Ranked Set Sampling
Joural of Moder Applied Statistical Methods Volume 13 Issue 2 Article 11-2014 Estimatio of Gumbel Parameters uder Raked Set Samplig Omar M. Yousef Al Balqa' Applied Uiversity, Zarqa, Jorda, abuyaza_o@yahoo.com
More informationEstimating the Population Mean using Stratified Double Ranked Set Sample
Estimatig te Populatio Mea usig Stratified Double Raked Set Sample Mamoud Syam * Kamarulzama Ibraim Amer Ibraim Al-Omari Qatar Uiversity Foudatio Program Departmet of Mat ad Computer P.O.Box (7) Doa State
More informationAbstract. Ranked set sampling, auxiliary variable, variance.
Hacettepe Joural of Mathematics ad Statistics Volume (), 1 A class of Hartley-Ross type Ubiased estimators for Populatio Mea usig Raked Set Samplig Lakhkar Kha ad Javid Shabbir Abstract I this paper, we
More informationModeling and Estimation of a Bivariate Pareto Distribution using the Principle of Maximum Entropy
Sri Laka Joural of Applied Statistics, Vol (5-3) Modelig ad Estimatio of a Bivariate Pareto Distributio usig the Priciple of Maximum Etropy Jagathath Krisha K.M. * Ecoomics Research Divisio, CSIR-Cetral
More informationControl Charts for Mean for Non-Normally Correlated Data
Joural of Moder Applied Statistical Methods Volume 16 Issue 1 Article 5 5-1-017 Cotrol Charts for Mea for No-Normally Correlated Data J. R. Sigh Vikram Uiversity, Ujjai, Idia Ab Latif Dar School of Studies
More informationRandom Variables, Sampling and Estimation
Chapter 1 Radom Variables, Samplig ad Estimatio 1.1 Itroductio This chapter will cover the most importat basic statistical theory you eed i order to uderstad the ecoometric material that will be comig
More informationExpectation and Variance of a random variable
Chapter 11 Expectatio ad Variace of a radom variable The aim of this lecture is to defie ad itroduce mathematical Expectatio ad variace of a fuctio of discrete & cotiuous radom variables ad the distributio
More informationGoodness-Of-Fit For The Generalized Exponential Distribution. Abstract
Goodess-Of-Fit For The Geeralized Expoetial Distributio By Amal S. Hassa stitute of Statistical Studies & Research Cairo Uiversity Abstract Recetly a ew distributio called geeralized expoetial or expoetiated
More informationBootstrap Intervals of the Parameters of Lognormal Distribution Using Power Rule Model and Accelerated Life Tests
Joural of Moder Applied Statistical Methods Volume 5 Issue Article --5 Bootstrap Itervals of the Parameters of Logormal Distributio Usig Power Rule Model ad Accelerated Life Tests Mohammed Al-Ha Ebrahem
More informationLecture 33: Bootstrap
Lecture 33: ootstrap Motivatio To evaluate ad compare differet estimators, we eed cosistet estimators of variaces or asymptotic variaces of estimators. This is also importat for hypothesis testig ad cofidece
More informationResampling Methods. X (1/2), i.e., Pr (X i m) = 1/2. We order the data: X (1) X (2) X (n). Define the sample median: ( n.
Jauary 1, 2019 Resamplig Methods Motivatio We have so may estimators with the property θ θ d N 0, σ 2 We ca also write θ a N θ, σ 2 /, where a meas approximately distributed as Oce we have a cosistet estimator
More informationElement sampling: Part 2
Chapter 4 Elemet samplig: Part 2 4.1 Itroductio We ow cosider uequal probability samplig desigs which is very popular i practice. I the uequal probability samplig, we ca improve the efficiecy of the resultig
More informationChapter 6 Sampling Distributions
Chapter 6 Samplig Distributios 1 I most experimets, we have more tha oe measuremet for ay give variable, each measuremet beig associated with oe radomly selected a member of a populatio. Hece we eed to
More informationPower Comparison of Some Goodness-of-fit Tests
Florida Iteratioal Uiversity FIU Digital Commos FIU Electroic Theses ad Dissertatios Uiversity Graduate School 7-6-2016 Power Compariso of Some Goodess-of-fit Tests Tiayi Liu tliu019@fiu.edu DOI: 10.25148/etd.FIDC000750
More informationThe Sampling Distribution of the Maximum. Likelihood Estimators for the Parameters of. Beta-Binomial Distribution
Iteratioal Mathematical Forum, Vol. 8, 2013, o. 26, 1263-1277 HIKARI Ltd, www.m-hikari.com http://d.doi.org/10.12988/imf.2013.3475 The Samplig Distributio of the Maimum Likelihood Estimators for the Parameters
More informationA RANK STATISTIC FOR NON-PARAMETRIC K-SAMPLE AND CHANGE POINT PROBLEMS
J. Japa Statist. Soc. Vol. 41 No. 1 2011 67 73 A RANK STATISTIC FOR NON-PARAMETRIC K-SAMPLE AND CHANGE POINT PROBLEMS Yoichi Nishiyama* We cosider k-sample ad chage poit problems for idepedet data i a
More informationAccess to the published version may require journal subscription. Published with permission from: Elsevier.
This is a author produced versio of a paper published i Statistics ad Probability Letters. This paper has bee peer-reviewed, it does ot iclude the joural pagiatio. Citatio for the published paper: Forkma,
More informationBayesian and E- Bayesian Method of Estimation of Parameter of Rayleigh Distribution- A Bayesian Approach under Linex Loss Function
Iteratioal Joural of Statistics ad Systems ISSN 973-2675 Volume 12, Number 4 (217), pp. 791-796 Research Idia Publicatios http://www.ripublicatio.com Bayesia ad E- Bayesia Method of Estimatio of Parameter
More informationLecture 19: Convergence
Lecture 19: Covergece Asymptotic approach I statistical aalysis or iferece, a key to the success of fidig a good procedure is beig able to fid some momets ad/or distributios of various statistics. I may
More informationStat 421-SP2012 Interval Estimation Section
Stat 41-SP01 Iterval Estimatio Sectio 11.1-11. We ow uderstad (Chapter 10) how to fid poit estimators of a ukow parameter. o However, a poit estimate does ot provide ay iformatio about the ucertaity (possible
More informationApproximate Confidence Interval for the Reciprocal of a Normal Mean with a Known Coefficient of Variation
Metodološki zvezki, Vol. 13, No., 016, 117-130 Approximate Cofidece Iterval for the Reciprocal of a Normal Mea with a Kow Coefficiet of Variatio Wararit Paichkitkosolkul 1 Abstract A approximate cofidece
More informationEstimation for Complete Data
Estimatio for Complete Data complete data: there is o loss of iformatio durig study. complete idividual complete data= grouped data A complete idividual data is the oe i which the complete iformatio of
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationG. R. Pasha Department of Statistics Bahauddin Zakariya University Multan, Pakistan
Deviatio of the Variaces of Classical Estimators ad Negative Iteger Momet Estimator from Miimum Variace Boud with Referece to Maxwell Distributio G. R. Pasha Departmet of Statistics Bahauddi Zakariya Uiversity
More informationEcon 325/327 Notes on Sample Mean, Sample Proportion, Central Limit Theorem, Chi-square Distribution, Student s t distribution 1.
Eco 325/327 Notes o Sample Mea, Sample Proportio, Cetral Limit Theorem, Chi-square Distributio, Studet s t distributio 1 Sample Mea By Hiro Kasahara We cosider a radom sample from a populatio. Defiitio
More informationChapter 2 The Monte Carlo Method
Chapter 2 The Mote Carlo Method The Mote Carlo Method stads for a broad class of computatioal algorithms that rely o radom sampligs. It is ofte used i physical ad mathematical problems ad is most useful
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More informationA goodness-of-fit test based on the empirical characteristic function and a comparison of tests for normality
A goodess-of-fit test based o the empirical characteristic fuctio ad a compariso of tests for ormality J. Marti va Zyl Departmet of Mathematical Statistics ad Actuarial Sciece, Uiversity of the Free State,
More informationA new distribution-free quantile estimator
Biometrika (1982), 69, 3, pp. 635-40 Prited i Great Britai 635 A ew distributio-free quatile estimator BY FRANK E. HARRELL Cliical Biostatistics, Duke Uiversity Medical Ceter, Durham, North Carolia, U.S.A.
More informationConfidence interval for the two-parameter exponentiated Gumbel distribution based on record values
Iteratioal Joural of Applied Operatioal Research Vol. 4 No. 1 pp. 61-68 Witer 2014 Joural homepage: www.ijorlu.ir Cofidece iterval for the two-parameter expoetiated Gumbel distributio based o record values
More informationConfidence Interval for Standard Deviation of Normal Distribution with Known Coefficients of Variation
Cofidece Iterval for tadard Deviatio of Normal Distributio with Kow Coefficiets of Variatio uparat Niwitpog Departmet of Applied tatistics, Faculty of Applied ciece Kig Mogkut s Uiversity of Techology
More informationTopic 9: Sampling Distributions of Estimators
Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be
More information1 Introduction to reducing variance in Monte Carlo simulations
Copyright c 010 by Karl Sigma 1 Itroductio to reducig variace i Mote Carlo simulatios 11 Review of cofidece itervals for estimatig a mea I statistics, we estimate a ukow mea µ = E(X) of a distributio by
More information7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals
7-1 Chapter 4 Part I. Samplig Distributios ad Cofidece Itervals 1 7- Sectio 1. Samplig Distributio 7-3 Usig Statistics Statistical Iferece: Predict ad forecast values of populatio parameters... Test hypotheses
More informationLet us give one more example of MLE. Example 3. The uniform distribution U[0, θ] on the interval [0, θ] has p.d.f.
Lecture 5 Let us give oe more example of MLE. Example 3. The uiform distributio U[0, ] o the iterval [0, ] has p.d.f. { 1 f(x =, 0 x, 0, otherwise The likelihood fuctio ϕ( = f(x i = 1 I(X 1,..., X [0,
More informationANOTHER WEIGHTED WEIBULL DISTRIBUTION FROM AZZALINI S FAMILY
ANOTHER WEIGHTED WEIBULL DISTRIBUTION FROM AZZALINI S FAMILY Sulema Nasiru, MSc. Departmet of Statistics, Faculty of Mathematical Scieces, Uiversity for Developmet Studies, Navrogo, Upper East Regio, Ghaa,
More informationDouble Stage Shrinkage Estimator of Two Parameters. Generalized Exponential Distribution
Iteratioal Mathematical Forum, Vol., 3, o. 3, 3-53 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/.9/imf.3.335 Double Stage Shrikage Estimator of Two Parameters Geeralized Expoetial Distributio Alaa M.
More informationMATH 320: Probability and Statistics 9. Estimation and Testing of Parameters. Readings: Pruim, Chapter 4
MATH 30: Probability ad Statistics 9. Estimatio ad Testig of Parameters Estimatio ad Testig of Parameters We have bee dealig situatios i which we have full kowledge of the distributio of a radom variable.
More informationInvestigating the Significance of a Correlation Coefficient using Jackknife Estimates
Iteratioal Joural of Scieces: Basic ad Applied Research (IJSBAR) ISSN 2307-4531 (Prit & Olie) http://gssrr.org/idex.php?joural=jouralofbasicadapplied ---------------------------------------------------------------------------------------------------------------------------
More informationProperties and Hypothesis Testing
Chapter 3 Properties ad Hypothesis Testig 3.1 Types of data The regressio techiques developed i previous chapters ca be applied to three differet kids of data. 1. Cross-sectioal data. 2. Time series data.
More informationStatistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.
Statistical Iferece (Chapter 10) Statistical iferece = lear about a populatio based o the iformatio provided by a sample. Populatio: The set of all values of a radom variable X of iterest. Characterized
More informationLecture 2: Monte Carlo Simulation
STAT/Q SCI 43: Itroductio to Resamplig ethods Sprig 27 Istructor: Ye-Chi Che Lecture 2: ote Carlo Simulatio 2 ote Carlo Itegratio Assume we wat to evaluate the followig itegratio: e x3 dx What ca we do?
More informationModified Ratio Estimators Using Known Median and Co-Efficent of Kurtosis
America Joural of Mathematics ad Statistics 01, (4): 95-100 DOI: 10.593/j.ajms.01004.05 Modified Ratio s Usig Kow Media ad Co-Efficet of Kurtosis J.Subramai *, G.Kumarapadiya Departmet of Statistics, Podicherry
More informationMaximum likelihood estimation from record-breaking data for the generalized Pareto distribution
METRON - Iteratioal Joural of Statistics 004, vol. LXII,. 3, pp. 377-389 NAGI S. ABD-EL-HAKIM KHALAF S. SULTAN Maximum likelihood estimatio from record-breakig data for the geeralized Pareto distributio
More informationLecture 7: Properties of Random Samples
Lecture 7: Properties of Radom Samples 1 Cotiued From Last Class Theorem 1.1. Let X 1, X,...X be a radom sample from a populatio with mea µ ad variace σ
More informationModified Lilliefors Test
Joural of Moder Applied Statistical Methods Volume 14 Issue 1 Article 9 5-1-2015 Modified Lilliefors Test Achut Adhikari Uiversity of Norther Colorado, adhi2939@gmail.com Jay Schaffer Uiversity of Norther
More informationEstimation of the Population Mean in Presence of Non-Response
Commuicatios of the Korea Statistical Society 0, Vol. 8, No. 4, 537 548 DOI: 0.535/CKSS.0.8.4.537 Estimatio of the Populatio Mea i Presece of No-Respose Suil Kumar,a, Sadeep Bhougal b a Departmet of Statistics,
More informationA statistical method to determine sample size to estimate characteristic value of soil parameters
A statistical method to determie sample size to estimate characteristic value of soil parameters Y. Hojo, B. Setiawa 2 ad M. Suzuki 3 Abstract Sample size is a importat factor to be cosidered i determiig
More informationComparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series
Applied Mathematical Scieces, Vol. 7, 03, o. 6, 3-337 HIKARI Ltd, www.m-hikari.com http://d.doi.org/0.988/ams.03.3430 Compariso Study of Series Approimatio ad Covergece betwee Chebyshev ad Legedre Series
More informationEstimation of Population Mean Using Co-Efficient of Variation and Median of an Auxiliary Variable
Iteratioal Joural of Probability ad Statistics 01, 1(4: 111-118 DOI: 10.593/j.ijps.010104.04 Estimatio of Populatio Mea Usig Co-Efficiet of Variatio ad Media of a Auxiliary Variable J. Subramai *, G. Kumarapadiya
More informationA General Family of Estimators for Estimating Population Variance Using Known Value of Some Population Parameter(s)
Rajesh Sigh, Pakaj Chauha, Nirmala Sawa School of Statistics, DAVV, Idore (M.P.), Idia Floreti Smaradache Uiversity of New Meico, USA A Geeral Family of Estimators for Estimatig Populatio Variace Usig
More informationBIOSTATISTICS. Lecture 5 Interval Estimations for Mean and Proportion. dr. Petr Nazarov
Microarray Ceter BIOSTATISTICS Lecture 5 Iterval Estimatios for Mea ad Proportio dr. Petr Nazarov 15-03-013 petr.azarov@crp-sate.lu Lecture 5. Iterval estimatio for mea ad proportio OUTLINE Iterval estimatios
More information1 Inferential Methods for Correlation and Regression Analysis
1 Iferetial Methods for Correlatio ad Regressio Aalysis I the chapter o Correlatio ad Regressio Aalysis tools for describig bivariate cotiuous data were itroduced. The sample Pearso Correlatio Coefficiet
More informationMOMENT-METHOD ESTIMATION BASED ON CENSORED SAMPLE
Vol. 8 o. Joural of Systems Sciece ad Complexity Apr., 5 MOMET-METHOD ESTIMATIO BASED O CESORED SAMPLE I Zhogxi Departmet of Mathematics, East Chia Uiversity of Sciece ad Techology, Shaghai 37, Chia. Email:
More informationESTIMATION AND PREDICTION BASED ON K-RECORD VALUES FROM NORMAL DISTRIBUTION
STATISTICA, ao LXXIII,. 4, 013 ESTIMATION AND PREDICTION BASED ON K-RECORD VALUES FROM NORMAL DISTRIBUTION Maoj Chacko Departmet of Statistics, Uiversity of Kerala, Trivadrum- 695581, Kerala, Idia M. Shy
More informationPARAMETER ESTIMATION BASED ON CUMU- LATIVE KULLBACK-LEIBLER DIVERGENCE
PARAMETER ESTIMATION BASED ON CUMU- LATIVE KULLBACK-LEIBLER DIVERGENCE Authors: Yaser Mehrali Departmet of Statistics, Uiversity of Isfaha, 81744 Isfaha, Ira (y.mehrali@sci.ui.ac.ir Majid Asadi Departmet
More informationImproved Class of Ratio -Cum- Product Estimators of Finite Population Mean in two Phase Sampling
Global Joural of Sciece Frotier Research: F Mathematics ad Decisio Scieces Volume 4 Issue 2 Versio.0 Year 204 Type : Double Blid Peer Reviewed Iteratioal Research Joural Publisher: Global Jourals Ic. (USA
More informationUsing the IML Procedure to Examine the Efficacy of a New Control Charting Technique
Paper 2894-2018 Usig the IML Procedure to Examie the Efficacy of a New Cotrol Chartig Techique Austi Brow, M.S., Uiversity of Norther Colorado; Bryce Whitehead, M.S., Uiversity of Norther Colorado ABSTRACT
More informationBull. Korean Math. Soc. 36 (1999), No. 3, pp. 451{457 THE STRONG CONSISTENCY OF NONLINEAR REGRESSION QUANTILES ESTIMATORS Seung Hoe Choi and Hae Kyung
Bull. Korea Math. Soc. 36 (999), No. 3, pp. 45{457 THE STRONG CONSISTENCY OF NONLINEAR REGRESSION QUANTILES ESTIMATORS Abstract. This paper provides suciet coditios which esure the strog cosistecy of regressio
More information6.3 Testing Series With Positive Terms
6.3. TESTING SERIES WITH POSITIVE TERMS 307 6.3 Testig Series With Positive Terms 6.3. Review of what is kow up to ow I theory, testig a series a i for covergece amouts to fidig the i= sequece of partial
More informationIt should be unbiased, or approximately unbiased. Variance of the variance estimator should be small. That is, the variance estimator is stable.
Chapter 10 Variace Estimatio 10.1 Itroductio Variace estimatio is a importat practical problem i survey samplig. Variace estimates are used i two purposes. Oe is the aalytic purpose such as costructig
More informationPOWER COMPARISON OF EMPIRICAL LIKELIHOOD RATIO TESTS: SMALL SAMPLE PROPERTIES THROUGH MONTE CARLO STUDIES*
Kobe Uiversity Ecoomic Review 50(2004) 3 POWER COMPARISON OF EMPIRICAL LIKELIHOOD RATIO TESTS: SMALL SAMPLE PROPERTIES THROUGH MONTE CARLO STUDIES* By HISASHI TANIZAKI There are various kids of oparametric
More informationTHE DATA-BASED CHOICE OF BANDWIDTH FOR KERNEL QUANTILE ESTIMATOR OF VAR
Iteratioal Joural of Iovative Maagemet, Iformatio & Productio ISME Iteratioal c2013 ISSN 2185-5439 Volume 4, Number 1, Jue 2013 PP. 17-24 THE DATA-BASED CHOICE OF BANDWIDTH FOR KERNEL QUANTILE ESTIMATOR
More informationJanuary 25, 2017 INTRODUCTION TO MATHEMATICAL STATISTICS
Jauary 25, 207 INTRODUCTION TO MATHEMATICAL STATISTICS Abstract. A basic itroductio to statistics assumig kowledge of probability theory.. Probability I a typical udergraduate problem i probability, we
More informationEDGEWORTH SIZE CORRECTED W, LR AND LM TESTS IN THE FORMATION OF THE PRELIMINARY TEST ESTIMATOR
Joural of Statistical Research 26, Vol. 37, No. 2, pp. 43-55 Bagladesh ISSN 256-422 X EDGEORTH SIZE CORRECTED, AND TESTS IN THE FORMATION OF THE PRELIMINARY TEST ESTIMATOR Zahirul Hoque Departmet of Statistics
More informationKLMED8004 Medical statistics. Part I, autumn Estimation. We have previously learned: Population and sample. New questions
We have previously leared: KLMED8004 Medical statistics Part I, autum 00 How kow probability distributios (e.g. biomial distributio, ormal distributio) with kow populatio parameters (mea, variace) ca give
More informationGoodness-Of-Fit For The Generalized Exponential Distribution. Abstract
Goodess-Of-Fit For The Geeralized Expoetial Distributio By Amal S. Hassa stitute of Statistical Studies & Research Cairo Uiversity Abstract Recetly a ew distributio called geeralized expoetial or expoetiated
More informationGUIDELINES ON REPRESENTATIVE SAMPLING
DRUGS WORKING GROUP VALIDATION OF THE GUIDELINES ON REPRESENTATIVE SAMPLING DOCUMENT TYPE : REF. CODE: ISSUE NO: ISSUE DATE: VALIDATION REPORT DWG-SGL-001 002 08 DECEMBER 2012 Ref code: DWG-SGL-001 Issue
More informationA Generalized Class of Estimators for Finite Population Variance in Presence of Measurement Errors
Joural of Moder Applied Statistical Methods Volume Issue Article 3 --03 A Geeralized Class of Estimators for Fiite Populatio Variace i Presece of Measuremet Errors Praas Sharma Baaras Hidu Uiversit, Varaasi,
More informationPublished online: 12 Jul 2013.
This article was dowloaded by: [Hadi Alizadeh Noughabi] O: 12 July 2013, At: 11:39 Publisher: Taylor & Fracis Iforma Ltd Registered i Eglad ad Wales Registered Number: 1072954 Registered office: Mortimer
More informationPrecise Rates in Complete Moment Convergence for Negatively Associated Sequences
Commuicatios of the Korea Statistical Society 29, Vol. 16, No. 5, 841 849 Precise Rates i Complete Momet Covergece for Negatively Associated Sequeces Dae-Hee Ryu 1,a a Departmet of Computer Sciece, ChugWoo
More informationElements of Statistical Methods Lots of Data or Large Samples (Ch 8)
Elemets of Statistical Methods Lots of Data or Large Samples (Ch 8) Fritz Scholz Sprig Quarter 2010 February 26, 2010 x ad X We itroduced the sample mea x as the average of the observed sample values x
More informationMathematical Modeling of Optimum 3 Step Stress Accelerated Life Testing for Generalized Pareto Distribution
America Joural of Theoretical ad Applied Statistics 05; 4(: 6-69 Published olie May 8, 05 (http://www.sciecepublishiggroup.com/j/ajtas doi: 0.648/j.ajtas.05040. ISSN: 6-8999 (Prit; ISSN: 6-9006 (Olie Mathematical
More informationA NEW METHOD FOR CONSTRUCTING APPROXIMATE CONFIDENCE INTERVALS FOR M-ESTU1ATES. Dennis D. Boos
.- A NEW METHOD FOR CONSTRUCTING APPROXIMATE CONFIDENCE INTERVALS FOR M-ESTU1ATES by Deis D. Boos Departmet of Statistics North Carolia State Uiversity Istitute of Statistics Mimeo Series #1198 September,
More information7.1 Convergence of sequences of random variables
Chapter 7 Limit Theorems Throughout this sectio we will assume a probability space (, F, P), i which is defied a ifiite sequece of radom variables (X ) ad a radom variable X. The fact that for every ifiite
More informationOrthogonal Gaussian Filters for Signal Processing
Orthogoal Gaussia Filters for Sigal Processig Mark Mackezie ad Kiet Tieu Mechaical Egieerig Uiversity of Wollogog.S.W. Australia Abstract A Gaussia filter usig the Hermite orthoormal series of fuctios
More informationMOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND.
XI-1 (1074) MOST PEOPLE WOULD RATHER LIVE WITH A PROBLEM THEY CAN'T SOLVE, THAN ACCEPT A SOLUTION THEY CAN'T UNDERSTAND. R. E. D. WOOLSEY AND H. S. SWANSON XI-2 (1075) STATISTICAL DECISION MAKING Advaced
More informationBasis for simulation techniques
Basis for simulatio techiques M. Veeraraghava, March 7, 004 Estimatio is based o a collectio of experimetal outcomes, x, x,, x, where each experimetal outcome is a value of a radom variable. x i. Defiitios
More informationThere is no straightforward approach for choosing the warmup period l.
B. Maddah INDE 504 Discrete-Evet Simulatio Output Aalysis () Statistical Aalysis for Steady-State Parameters I a otermiatig simulatio, the iterest is i estimatig the log ru steady state measures of performace.
More informationJournal of Multivariate Analysis. Superefficient estimation of the marginals by exploiting knowledge on the copula
Joural of Multivariate Aalysis 102 (2011) 1315 1319 Cotets lists available at ScieceDirect Joural of Multivariate Aalysis joural homepage: www.elsevier.com/locate/jmva Superefficiet estimatio of the margials
More information1.010 Uncertainty in Engineering Fall 2008
MIT OpeCourseWare http://ocw.mit.edu.00 Ucertaity i Egieerig Fall 2008 For iformatio about citig these materials or our Terms of Use, visit: http://ocw.mit.edu.terms. .00 - Brief Notes # 9 Poit ad Iterval
More informationIntroducing a Novel Bivariate Generalized Skew-Symmetric Normal Distribution
Joural of mathematics ad computer Sciece 7 (03) 66-7 Article history: Received April 03 Accepted May 03 Available olie Jue 03 Itroducig a Novel Bivariate Geeralized Skew-Symmetric Normal Distributio Behrouz
More informationOn a Smarandache problem concerning the prime gaps
O a Smaradache problem cocerig the prime gaps Felice Russo Via A. Ifate 7 6705 Avezzao (Aq) Italy felice.russo@katamail.com Abstract I this paper, a problem posed i [] by Smaradache cocerig the prime gaps
More informationAClassofRegressionEstimatorwithCumDualProductEstimatorAsIntercept
Global Joural of Sciece Frotier Research: F Mathematics ad Decisio Scieces Volume 15 Issue 3 Versio 1.0 Year 2015 Type : Double Blid Peer Reviewed Iteratioal Research Joural Publisher: Global Jourals Ic.
More informationModule 1 Fundamentals in statistics
Normal Distributio Repeated observatios that differ because of experimetal error ofte vary about some cetral value i a roughly symmetrical distributio i which small deviatios occur much more frequetly
More informationSince X n /n P p, we know that X n (n. Xn (n X n ) Using the asymptotic result above to obtain an approximation for fixed n, we obtain
Assigmet 9 Exercise 5.5 Let X biomial, p, where p 0, 1 is ukow. Obtai cofidece itervals for p i two differet ways: a Sice X / p d N0, p1 p], the variace of the limitig distributio depeds oly o p. Use the
More informationECONOMETRIC THEORY. MODULE XIII Lecture - 34 Asymptotic Theory and Stochastic Regressors
ECONOMETRIC THEORY MODULE XIII Lecture - 34 Asymptotic Theory ad Stochastic Regressors Dr. Shalabh Departmet of Mathematics ad Statistics Idia Istitute of Techology Kapur Asymptotic theory The asymptotic
More informationConvergence of random variables. (telegram style notes) P.J.C. Spreij
Covergece of radom variables (telegram style otes).j.c. Spreij this versio: September 6, 2005 Itroductio As we kow, radom variables are by defiitio measurable fuctios o some uderlyig measurable space
More informationEcon 325 Notes on Point Estimator and Confidence Interval 1 By Hiro Kasahara
Poit Estimator Eco 325 Notes o Poit Estimator ad Cofidece Iterval 1 By Hiro Kasahara Parameter, Estimator, ad Estimate The ormal probability desity fuctio is fully characterized by two costats: populatio
More informationStatistical Test for Multi-dimensional Uniformity
Florida Iteratioal Uiversity FIU Digital Commos FIU Electroic Theses ad Dissertatios Uiversity Graduate School -0-20 Statistical Test for Multi-dimesioal Uiformity Tieyog Hu Florida Iteratioal Uiversity,
More informationAn Introduction to Randomized Algorithms
A Itroductio to Radomized Algorithms The focus of this lecture is to study a radomized algorithm for quick sort, aalyze it usig probabilistic recurrece relatios, ad also provide more geeral tools for aalysis
More informationSimulation. Two Rule For Inverting A Distribution Function
Simulatio Two Rule For Ivertig A Distributio Fuctio Rule 1. If F(x) = u is costat o a iterval [x 1, x 2 ), the the uiform value u is mapped oto x 2 through the iversio process. Rule 2. If there is a jump
More informationEXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY
EXAMINATIONS OF THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA, 016 MODULE : Statistical Iferece Time allowed: Three hours Cadidates should aswer FIVE questios. All questios carry equal marks. The umber
More informationBecause it tests for differences between multiple pairs of means in one test, it is called an omnibus test.
Math 308 Sprig 018 Classes 19 ad 0: Aalysis of Variace (ANOVA) Page 1 of 6 Itroductio ANOVA is a statistical procedure for determiig whether three or more sample meas were draw from populatios with equal
More informationHypothesis Testing. Evaluation of Performance of Learned h. Issues. Trade-off Between Bias and Variance
Hypothesis Testig Empirically evaluatig accuracy of hypotheses: importat activity i ML. Three questios: Give observed accuracy over a sample set, how well does this estimate apply over additioal samples?
More information4. Partial Sums and the Central Limit Theorem
1 of 10 7/16/2009 6:05 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 4. Partial Sums ad the Cetral Limit Theorem The cetral limit theorem ad the law of large umbers are the two fudametal theorems
More informationIntroductory statistics
CM9S: Machie Learig for Bioiformatics Lecture - 03/3/06 Itroductory statistics Lecturer: Sriram Sakararama Scribe: Sriram Sakararama We will provide a overview of statistical iferece focussig o the key
More informationSelf-normalized deviation inequalities with application to t-statistic
Self-ormalized deviatio iequalities with applicatio to t-statistic Xiequa Fa Ceter for Applied Mathematics, Tiaji Uiversity, 30007 Tiaji, Chia Abstract Let ξ i i 1 be a sequece of idepedet ad symmetric
More informationInformation-based Feature Selection
Iformatio-based Feature Selectio Farza Faria, Abbas Kazeroui, Afshi Babveyh Email: {faria,abbask,afshib}@staford.edu 1 Itroductio Feature selectio is a topic of great iterest i applicatios dealig with
More informationOn stratified randomized response sampling
Model Assisted Statistics ad Applicatios 1 (005,006) 31 36 31 IOS ress O stratified radomized respose samplig Jea-Bok Ryu a,, Jog-Mi Kim b, Tae-Youg Heo c ad Chu Gu ark d a Statistics, Divisio of Life
More information