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Techniques of event history modeling: new approaches to causal analysis

Author: Blossfeld, Hans-Peter ; Rohwer, GötzPublisher: Lawrence Erlbaum Associates, 2002.Edition: 2nd ed.Language: EnglishDescription: 310 p. : Ill. ; 23 cm.ISBN: 0805840915Type of document: BookBibliography/Index: Includes bibliographical references and index
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Item type Current location Collection Call number Status Date due Barcode Item holds
Book Asia Campus
Main Collection
Print H61 .B56 2002
(Browse shelf)
900093778
Available 900093778
Total holds: 0

Includes bibliographical references and index

Digitized

Techniques of event history modeling Techniques of event history modeling Contents Preface vii 1 Introduction 1 1.1 Causal Modeling and Observation Plans . . . . . . . . . . . . 4 1.1.1 Cross-Sectional Data . . . . . . . . . . . . . . . . . . . 5 1.1.2 Panel Data . . . . . . . . . . . . . . . . . . . . . . . . 13 1.1.3 Event History Data . . . . . . . . . . . . . . . . . . . 19 1.2 Event History Analysis and Causal Modeling . . . . . . . . . 21 2 Event History Data Structures 38 2.1 Basic Terminology . . . . . . . . . . . . . . . . . . . . . . . . 38 2.2 Event History Data Organization . . . . . . . . . . . . . . . . 42 -3 Nonparametric Descriptive Methods 56 3.1 Life Table Method . . . . . . . . . . . . . . . . . . . . . . . . 56 3.2 Product-Limit Estimation . . . . . . . . . . . . . . . . . . . . 71 3.3 Comparing Survivor Functions . . . . . . . . . . . . . . . . . 76 4 Exponential Transition Rate Models 4.1 The Basic Exponential Model . . . . . . 4.1.1 Maximum Likelihood Estimation 4.1.2 Models without Covariates . . . 4.1.3 Time-Constant Covariates . . . . 4.2 Models with Multiple Destinations . . . 4.3 Models with Multiple Episodes . . . . . ............ ............ . . . . ......... ......... ......... ......... 86 87 88 . . 91 . . 95 . . 101 . . 111 5 Piecewise Constant Exponential Models 5.1 The Basic Model . . . . . . . . . . . . . . . . 5.2 Models without Covariates . . . . . . . . . . . 5.3 Models with Proportional Covariate Effects . 5.4 Models with Period-Specific Effects . . . . . . . . . . . . . . . 120 . . . . . . . . . 122 . . . . . . . . .125 . . . . . . . . . 125 120 6 Exponential Models with Time-Dependent Covariates 131 6.1 Parallel and Interdependent Processes . . . . . . . . . . . . . 131 6.2 Interdependent Processes: The System Approach . . . . . . . 134 6.3 Interdependent Processes: The Causal Approach . . . . . . . 138 . 6.4 Episode Splitting with Qualitative Covariates . . . . . . . . . 140 6.5 Episode Splitting with Quantitative Covariates . . . . . . . . 152 6.6 Application Examples . . . . . . . . . . . . . . . . . . . . . . 157 7 Parametric Models of Time-Dependence 7.1 Interpretation of Time-Dependence . . . . 7.2 Gompertz-Makeham Models . . . . . . . . 7.3 Weibull Models . . . . . . . . . . . . . . . 7.4 Log-Logistic Models . . . . . . . . . . . . 7.5 Log-Normal Models . . . . . . . . . . . . 7.6 Sickle Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 . . . . . . . . . . . 191 . . . . . . . . . . . 197 . . . . . . . . . . . 202 . . . . . . . . . . . 208 . . . . . . . . . . . 177 176 8 Methods to Check Parametric Assumptions 213 8.1 Simple Graphical Methods . . . . . . . . . . . . . . . . . . . . 213 8.2 Pseudoresiduals . . . . . . . . . . . . . . . . . . . . . . . . . . 219 9 Semi-parametric Transition Rate Models 9.1 Partial Likelihood Estimation . . . . . . . 9.2 Time-Dependent Covariates . . . . . . . . 9.3 The Proportionality Assumption . . . . . 9.4 Baseline Rates and Survivor Functions . . 9.5 Application Example . . . . . . . . . . . . 228 . . . . . . . . . . . 229 . . . . . . . . . . . 234 . . . . . . . . . . . 240 . . . . . . . . . . . 248 . . . . . . . . . . . 251 10 Problems of Model Specification 255 10.1 Unobserved Heterogeneity . . . . . . . . . . . . . . . . . . . . 255 10.2 Models with a Mixture Distribution . . . . . . . . . . . . . . 261 10.2.1 Models with a Gamma Mixture . . . . . . . . . . . . . 264 10.2.2 Exponential Models with a Gamma Mixture . . . . . 267 10.2.3 Weibull Models with a Gamma Mixture . . . . . . . . 269 10.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .274 Appendix A: Basic Information About TDA References About the Authors Index 279 282 304 305

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