Statistical Analysis of Doubly Truncated Data

With Applications in R

AvJacobo de Uña-Álvarez,Rosa M. Crujeiras

Inbunden, Engelska, 2021

Del 64 i serien Wiley Series in Probability and Statistics

956 kr

Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

Beskrivning

A thorough treatment of the statistical methods used to analyze doubly truncated data In The Statistical Analysis of Doubly Truncated Data, an expert team of statisticians delivers an up-to-date review of existing methods used to deal with randomly truncated data, with a focus on the challenging problem of random double truncation. The authors comprehensively introduce doubly truncated data before moving on to discussions of the latest developments in the field. The book offers readers examples with R code along with real data from astronomy, engineering, and the biomedical sciences to illustrate and highlight the methods described within. Linear regression models for doubly truncated responses are provided and the influence of the bandwidth in the performance of kernel-type estimators, as well as guidelines for the selection of the smoothing parameter, are explored. Fully nonparametric and semiparametric estimators are explored and illustrated with real data. R code for reproducing the data examples is also provided. The book also offers: A thorough introduction to the existing methods that deal with randomly truncated dataComprehensive explorations of linear regression models for doubly truncated responsesPractical discussions of the influence of bandwidth in the performance of kernel-type estimators and guidelines for the selection of the smoothing parameterIn-depth examinations of nonparametric and semiparametric estimatorsPerfect for statistical professionals with some background in mathematical statistics, biostatisticians, and mathematicians with an interest in survival analysis and epidemiology, The Statistical Analysis of Doubly Truncated Data is also an invaluable addition to the libraries of biomedical scientists and practitioners, as well as postgraduate students studying survival analysis.

Produktinformation

Utgivningsdatum:2021-12-09
Mått:152 x 229 x 18 mm
Vikt:397 g
Format:Inbunden
Språk:Engelska
Serie:Wiley Series in Probability and Statistics
Antal sidor:192
Förlag:John Wiley & Sons Inc
ISBN:9781119951377

Utforska kategorier

Mer om författaren

Jacobo de Uña-Álvarez is Professor at the Department of Statistics and Operations Research, University of Vigo, 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.

Innehållsförteckning

Preface xiList of Abbreviations xiiiNotation xv1 Introduction 11.1 Random Truncation 11.2 One-sided Truncation 21.2.1 Left-truncation 21.2.2 Right-truncation 21.2.3 Truncation vs. Censoring 31.3 Double Truncation 31.4 Real Data Examples 51.4.1 Childhood Cancer Data 51.4.2 AIDS Blood Transfusion Data 61.4.3 Equipment-S Rounded Failure Time Data 71.4.4 Quasar Data 71.4.5 Parkinson’s Disease Data 81.4.6 Acute Coronary Syndrome Data 9References 102 One-Sample Problems 132.1 Nonparametric Estimation of a Distribution Function 132.1.1 The NPMLE 142.1.2 Numerical Algorithms for Computing the NPMLE 212.1.3 Theoretical Properties of the NPMLE 242.1.4 Standard Errors and Confidence Limits 362.2 Semiparametric and Parametric Approaches 432.2.1 Semiparametric Approach 442.2.2 Parametric Approach 522.3 R Code for the Examples 562.3.1 Code for Example 2.1.8 562.3.2 Code for Examples 2.1.11 and 2.1.13 562.3.3 Code for Example 2.1.14 582.3.4 Code for Example 2.1.15 592.3.5 Code for Example 2.1.22 602.3.6 Code for Example 2.2.6 612.3.7 Code for Example 2.2.8 62References 653 Smoothing Methods 693.1 Some Background in Kernel Estimation 693.2 Estimating the Density Function 713.3 Asymptotic Properties 713.4 Data-driven Bandwidth Selection 773.4.1 Normal Reference Bandwidth Selection 783.4.2 Plug-in Bandwidth Selection 793.4.3 Least-squares Cross-validation Bandwidth Selection 803.4.4 Smoothed Bootstrap Bandwidth Selection 813.4.5 Bandwidth Selectors in Practice 823.5 Further Issues in Kernel Density Estimation 883.6 Estimating the Hazard Function 903.7 R Code for the Examples 983.7.1 Code for Example 3.2.1 983.7.2 Code for Examples 3.3.4 and 3.3.5 993.7.3 Code for Examples 3.4.2 and 3.4.3 1003.7.4 Code for Example 3.5.1 1023.7.5 Code for Example 3.6.4 1043.7.6 Code for Example 3.6.5 105References 1064 Regression Analysis 1094.1 Observational Bias in Regression 1094.2 Proportional Hazards Regression 1144.3 Accelerated Failure Time Regression 1174.4 Nonparametric Regression 1214.5 R Code for the Examples 1264.5.1 Code for Example 4.1.1 1264.5.2 Code for Example 4.1.4 1264.5.3 Code for Example 4.2.4 1274.5.4 Code for Example 4.3.2 1274.5.5 Code for Example 4.4.2 128References 1295 Further Topics 1315.1 Two-Sample Problems 1325.2 Competing Risks 1375.2.1 Cumulative Incidences 1395.2.2 Regression Models for Competing Risks 1425.3 Testing for Quasi-independence 1465.4 Dependent Truncation 1505.5 R Code for the Examples 1575.5.1 Code for Example 5.1.3 1575.5.2 Code for Example 5.2.4 1595.5.3 Code for Example 5.2.6 1605.5.4 Code for Example 5.3.1 1615.5.5 Code for Example 5.4.3 161References 162A Packages and Functions in R 165A.1 Computing the NPMLE and Standard Errors 166A.2 Assessing the Existence and Uniqueness of the NPMLE 167A.3 Semiparametric and Parametric Estimation 168A.4 Kernel Estimation 168A.5 Regression Analysis 169A.6 Competing Risks 169A.7 Simulating Data 170A.8 Testing Quasi-independence 170A.9 Dependent Truncation 170References 171Index 173