LEADER 05539nam 2200757 a 450 001 9910808680503321 005 20240313144139.0 010 $a1-118-55807-3 010 $a1-118-60213-7 010 $a1-118-60198-X 010 $a1-299-18765-X 035 $a(CKB)2550000001005899 035 $a(EBL)1124667 035 $a(OCoLC)828298924 035 $a(SSID)ssj0000831968 035 $a(PQKBManifestationID)11440105 035 $a(PQKBTitleCode)TC0000831968 035 $a(PQKBWorkID)10882193 035 $a(PQKB)10083103 035 $a(OCoLC)828424582 035 $a(MiAaPQ)EBC1124667 035 $a(Au-PeEL)EBL1124667 035 $a(CaPaEBR)ebr10660569 035 $a(CaONFJC)MIL450015 035 $a(PPN)19069713X 035 $a(EXLCZ)992550000001005899 100 $a20100928d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aNon-parametric tests for censored data$b[electronic resource] /$fVilijandas Bagdonavic?ius, Julius Kruopis, Mikhail S. Nikulin 205 $a1st ed. 210 $aLondon $cISTE ;$aHoboken, N.J. $cWiley$d2011 215 $a1 online resource (253 p.) 225 1 $aISTE 300 $aDescription based upon print version of record. 311 $a1-84821-289-5 320 $aIncludes bibliographical references and index. 327 $aCover; Non-parametric Tests for Censored Data; Title Page; Copyright Page; Table of Contents; Preface; Terms and Notation; Chapter 1. Censored and Truncated Data; 1.1. Right-censored data; 1.2. Left truncation; 1.3. Left truncation and right censoring; 1.4. Nelson-Aalen and Kaplan-Meier estimators; 1.5. Bibliographic notes; Chapter 2. Chi-squared Tests; 2.1. Chi-squared test for composite hypothesis; 2.2. Chi-squared test for exponential distributions; 2.3. Chi-squared tests for shape-scale distribution families; 2.3.1. Chi-squared test for the Weibull distribution 327 $a2.3.2. Chi-squared tests for the loglogistic distribution2.3.3. Chi-squared test for the lognormal distribution; 2.4. Chi-squared tests for other families; 2.4.1. Chi-squared test for the Gompertz distribution; 2.4.2. Chi-squared test for distribution with hyperbolic hazard function; 2.4.3. Bibliographic notes; 2.5. Exercises; 2.6. Answers; Chapter 3. Homogeneity Tests for Independent Populations; 3.1. Data; 3.2. Weighted logrank statistics; 3.3. Logrank test statistics as weighted sums of differences between observed and expected number of failures; 3.4. Examples of weights 327 $a3.5. Weighted logrank statistics as modified score statistics3.6. The first two moments of weighted logrank statistics; 3.7. Asymptotic properties of weighted logrank statistics; 3.8. Weighted logrank tests; 3.9. Homogeneity testing when alternatives are crossings of survival functions; 3.9.1. Alternatives; 3.9.2. Modified score statistics; 3.9.3. Limit distribution of the modified score statistics; 3.9.4. Homogeneity tests against crossing survival functions alternatives; 3.9.5. Bibliographic notes; 3.10. Exercises; 3.11. Answers; Chapter 4. Homogeneity Tests for Related Populations 327 $a4.1. Paired samples4.1.1. Data; 4.1.2. Test statistics; 4.1.3. Asymptotic distribution of the test statistic; 4.1.4. The test; 4.2. Logrank-type tests for homogeneity of related k > 2 samples; 4.3. Homogeneity tests for related samples against crossing marginal survival functions alternatives; 4.3.1. Bibliographic notes; 4.4. Exercises; 4.5. Answers; Chapter 5. Goodness-of-fit for Regression Models; 5.1. Goodness-of-fit for the semi-parametric Cox model; 5.1.1. The Cox model; 5.1.2. Alternatives to the Cox model based on expanded models; 5.1.3. The data and the modified score statistics 327 $a5.1.4. Asymptotic distribution of the modified score statistic5.1.5. Tests; 5.2. Chi-squared goodness-of-fit tests for parametric AFT models; 5.2.1. Accelerated failure time model; 5.2.2. Parametric AFT model; 5.2.3. Data; 5.2.4. Idea of test construction; 5.2.5. Asymptotic distribution of Hn and Z; 5.2.6. Test statistics; 5.3. Chi-squared test for the exponential AFT model.; 5.4. Chi-squared tests for scale-shape AFT models.; 5.4.1. Chi-squared test for the Weibull AFT model; 5.4.2. Chi-squared test for the lognormal AFT model; 5.4.3. Chi-squared test for the loglogistic AFT model 327 $a5.5. Bibliographic notes 330 $aThis book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed. 410 0$aISTE 606 $aNonparametric statistics 606 $aStatistical hypothesis testing 606 $aCensored observations (Statistics) 615 0$aNonparametric statistics. 615 0$aStatistical hypothesis testing. 615 0$aCensored observations (Statistics) 676 $a519.5 686 $aMAT003000$2bisacsh 700 $aBagdonavic?ius$b V$g(Vilijandas)$0877332 701 $aKruopis$b Julius$01594266 701 $aNikulin$b M. S$g(Mikhail Stepanovich)$059991 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910808680503321 996 $aNon-parametric tests for censored data$93914760 997 $aUNINA