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020 _a1119951372
020 _a9781119500476
_q(epub)
020 _a1119500478
020 _a9781119500483
_q(adobe pdf)
020 _a1119500486
020 _z9781119951377
_q(cloth)
020 _a9781119500469
_q(electronic bk.)
020 _a111950046X
_q(electronic bk.)
035 _a(OCoLC)1286072415
_z(OCoLC)1285715756
_z(OCoLC)1292553530
040 _aDLC
_beng
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041 _aeng
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_223/eng/20211101
100 1 _aU�na-�Alvarez, Jacobo de,
_d1972-
_0http://id.loc.gov/authorities/names/n2021183317
_eauthor.
245 1 4 _aThe statistical analysis of doubly truncated data :
_bwith applications in R /
_cJacobo de Una-Alvarez, Carla Moreira, Rosa M. Crujeiras.
264 1 _aHoboken, NJ :
_bWiley,
_c©2022.
300 _a1 online resource.
336 _atext
_btxt
_2rdacontent.
337 _acomputer
_bc
_2rdamedia.
338 _aonline resource
_bcr
_2rdacarrier.
490 1 _aWiley series in probability and statistics.
504 _aIncludes bibliographical references and index.
505 0 _aTable of Contents Preface xi List of Abbreviations xiii Notation xv 1 Introduction 1 1.1 Random Truncation 1 1.2 One-sided Truncation 2 1.2.1 Left-truncation 2 1.2.2 Right-truncation 2 1.2.3 Truncation vs. Censoring 3 1.3 Double Truncation 3 1.4 Real Data Examples 5 1.4.1 Childhood Cancer Data 5 1.4.2 AIDS Blood Transfusion Data 6 1.4.3 Equipment-S Rounded Failure Time Data 7 1.4.4 Quasar Data 7 1.4.5 Parkinson’s Disease Data 8 1.4.6 Acute Coronary Syndrome Data 9 References 10 2 One-Sample Problems 13 2.1 Nonparametric Estimation of a Distribution Function 13 2.1.1 The NPMLE 14 2.1.2 Numerical Algorithms for Computing the NPMLE 21 2.1.3 Theoretical Properties of the NPMLE 24 2.1.4 Standard Errors and Confidence Limits 36 2.2 Semiparametric and Parametric Approaches 43 2.2.1 Semiparametric Approach 44 2.2.2 Parametric Approach 52 2.3 R Code for the Examples 56 2.3.1 Code for Example 2.1.8 56 2.3.2 Code for Examples 2.1.11 and 2.1.13 56 2.3.3 Code for Example 2.1.14 58 2.3.4 Code for Example 2.1.15 59 2.3.5 Code for Example 2.1.22 60 2.3.6 Code for Example 2.2.6 61 2.3.7 Code for Example 2.2.8 62 References 65 3 Smoothing Methods 69 3.1 Some Background in Kernel Estimation 69 3.2 Estimating the Density Function 71 3.3 Asymptotic Properties 71 3.4 Data-driven Bandwidth Selection 77 3.4.1 Normal Reference Bandwidth Selection 78 3.4.2 Plug-in Bandwidth Selection 79 3.4.3 Least-squares Cross-validation Bandwidth Selection 80 3.4.4 Smoothed Bootstrap Bandwidth Selection 81 3.4.5 Bandwidth Selectors in Practice 82 3.5 Further Issues in Kernel Density Estimation 88 3.6 Estimating the Hazard Function 90 3.7 R Code for the Examples 98 3.7.1 Code for Example 3.2.1 98 3.7.2 Code for Examples 3.3.4 and 3.3.5 99 3.7.3 Code for Examples 3.4.2 and 3.4.3 100 3.7.4 Code for Example 3.5.1 102 3.7.5 Code for Example 3.6.4 104 3.7.6 Code for Example 3.6.5 105 References 106 4 Regression Analysis 109 4.1 Observational Bias in Regression 109 4.2 Proportional Hazards Regression 114 4.3 Accelerated Failure Time Regression 117 4.4 Nonparametric Regression 121 4.5 R Code for the Examples 126 4.5.1 Code for Example 4.1.1 126 4.5.2 Code for Example 4.1.4 126 4.5.3 Code for Example 4.2.4 127 4.5.4 Code for Example 4.3.2 127 4.5.5 Code for Example 4.4.2 128 References 129 5 Further Topics 131 5.1 Two-Sample Problems 132 5.2 Competing Risks 137 5.2.1 Cumulative Incidences 139 5.2.2 Regression Models for Competing Risks 142 5.3 Testing for Quasi-independence 146 5.4 Dependent Truncation 150 5.5 R Code for the Examples 157 5.5.1 Code for Example 5.1.3 157 5.5.2 Code for Example 5.2.4 159 5.5.3 Code for Example 5.2.6 160 5.5.4 Code for Example 5.3.1 161 5.5.5 Code for Example 5.4.3 161 References 162 A Packages and Functions in R 165 A.1 Computing the NPMLE and Standard Errors 166 A.2 Assessing the Existence and Uniqueness of the NPMLE 167 A.3 Semiparametric and Parametric Estimation 168 A.4 Kernel Estimation 168 A.5 Regression Analysis 169 A.6 Competing Risks 169 A.7 Simulating Data 170 A.8 Testing Quasi-independence 170 A.9 Dependent Truncation 170 References 171 Index 173
520 _a"This book is the result of a long-standing collaboration among the three authors, which began when Carla Moreira was a PhD student under the supervision of Jacobo de Un�a-�Alvarez. Carla successfully defended her thesis, entitled 'The Statistical Analysis of Doubly Truncated Data: New Methods, Software Development, and Biomedical Applications', at the Universidade de Vigo in July 2010. At that time, just a reduced group of people seemed to be aware of the importance of random double truncation. Research papers on this topic were scarce before 2010, with the contribution by Bradley Efron and Vahe Petrosian in 1999 as the most relevant one. And, of course, no software was available. So, for us, it was a risky and exciting research exercise to embrace such an initiative. This book aims to serve as a companion for those ones interested in learning about doubly truncated data analysis and inference, presenting a wide range of tools for estimating distribution and regression models. All the methods presented in this book are accompanied by real data and simulated examples and, at the end of each chapter, the reader will find the do-it-yourself code, mostly based on DTDA package. This book is not written with the aim of being just read: its main purpose is to invite the reader to think, explore and experience"--
_cProvided by publisher.
545 0 _aAbout the Authors Jacobo de Uña-Álvarez is Professor at the Department of Statistics and Operations Research, University of Vigo in Spain. Carla Moreira is Associate Researcher at the Centre of Mathematics -School of Sciences, University of Minho in Portugal. She is also affiliated to the Statistical Inference, Decision and Operations Research group, University of Vigo, Spain, and to the Epidemiology Research unit- Institute of Public Health, University of Porto, Portugal. Rosa M. Crujeiras is Associate Professor at the Department of Statistics, Mathematical Analysis and Optimization, University of Santiago de Compostela, Spain.
650 1 2 _aBiometry
_xmethods.
_0https://id.nlm.nih.gov/mesh/D001699Q000379.
650 1 2 _aStatistics as Topic.
_0https://id.nlm.nih.gov/mesh/D013223.
650 2 2 _aData Interpretation, Statistical.
_0https://id.nlm.nih.gov/mesh/D003627.
650 2 2 _aProgramming Languages.
_0https://id.nlm.nih.gov/mesh/D011381.
650 2 2 _aModels, Statistical.
_0https://id.nlm.nih.gov/mesh/D015233.
650 0 _aBiometry
_0http://id.loc.gov/authorities/subjects/sh85014244
_xMethods.
_0http://id.loc.gov/authorities/subjects/sh2006007982.
650 0 _aStatistics
_xComputer programs.
_0http://id.loc.gov/authorities/subjects/sh85127582.
650 0 _aR (Computer program language)
_0http://id.loc.gov/authorities/subjects/sh2002004407.
655 4 _aElectronic books.
655 7 _aMethods (Music)
_2fast
_0http://id.worldcat.org/fast/1423850.
700 1 _aMoreira, Carla,
_d1972-
_0http://id.loc.gov/authorities/names/n2021183318
_eauthor.
700 1 _aCrujeiras, Rosa M.
_q(Rosa Maria),
_d1978-
_0http://id.loc.gov/authorities/names/n2021183319
_eauthor.
830 0 _aWiley series in probability and statistics.
_0http://id.loc.gov/authorities/names/n98048834.
856 4 0 _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119500469
_yFull text is available at Wiley Online Library Click here to view
942 _2ddc
_cER