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Introduction Introduction
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Motivation Motivation
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Statement of the Problems: Censoring and Time-varying Covariates Statement of the Problems: Censoring and Time-varying Covariates
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Illustration of the Censoring Problem Illustration of the Censoring Problem
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Initial Statement of the Solution to the Problems Initial Statement of the Solution to the Problems
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The Hazard-Rate Framework: Discrete-time Formulation The Hazard-Rate Framework: Discrete-time Formulation
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The Hazard-Rate Framework: Continuous-time Formulation Basic Concepts The Hazard-Rate Framework: Continuous-time Formulation Basic Concepts
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Continuous-time Formulations Continuous-time Formulations
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Specifications and Estimation Specifications and Estimation
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Examples Examples
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Relationship Between the Rate and Probability of Event in a Period Relationship Between the Rate and Probability of Event in a Period
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Time-independent Covariates Time-independent Covariates
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Specifications, Estimates, and Size of Coefficients Specifications, Estimates, and Size of Coefficients
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Illustration Illustration
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Time-dependent Covariates Basic Ideas Time-dependent Covariates Basic Ideas
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Exogeneity of Covariates Exogeneity of Covariates
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The Survivor Function The Survivor Function
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Data Management Data Management
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Illustration Illustration
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Observability of the Dependent Variable Observability of the Dependent Variable
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Repeated Events Repeated Events
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Multistate Processes: Discrete State-Space Multistate Processes: Discrete State-Space
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Multistate Processes: Continuous State-Space Multistate Processes: Continuous State-Space
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Conclusion Conclusion
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Notes Notes
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References References
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23 Analyzing Event History Data
Get accessTrond Peterson, Department of Sociology, University of California-Berkeley
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Published:01 October 2013
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Abstract
The chapter gives an instruction to event history analysis. The central goals are first to justify why what perhaps must be considered an unusual modeling approach is needed and next to explicate in some detail what the key ideas from probability theory are and how these ideas solve the problems that arise when using more standard techniques such as regression analysis for continuous dependent variables or logit analysis for binary dependent variables. Elaborations for how to take account of measured variables are given. It elaborates on what the dependent variable is in event history analysis, on the framework for repeated event processes, multi-state processes, and continuous-state space processes.
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