Event History Analysis: Statistical Theory and Application in the Social Sciences
Serving as both a student textbook and a professional reference/handbook, this volume explores the statistical methods of examining time intervals between successive state transitions or events. Examples include: survival rates of patients in medical studies, unemployment periods in economic studies, or the period of time it takes a criminal to break the law after his release in a criminological study. The authors illustrate the entire research path required in the application of event-history analysis, from the initial problems of recording event-oriented data to the specific questions of data organization, to the concrete application of available program packages and the interpretation of the obtained results.
Event History Analysis:
* makes didactically accessible the inclusion of covariates in semi-parametric and parametric regression models based upon concrete examples
* presents the unabbreviated close relationship underlying statistical theory
* details parameter-free methods of analysis of event-history data and the possibilities of their graphical presentation
* discusses specific problems of multi-state and multi-episode models
* introduces time-varying covariates and the question of unobserved population heterogeneity
* demonstrates, through examples, how to implement hypotheses tests and how to choose the right model.
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Aim and Structure of the Book
The Statistical Theory of Event History Analysis
Data Organization and Descriptive Methods
4 other sections not shown
analyzed ANTILOG ANTILOG applied assumption asymptotic baseline hazard rate calculated censored observations CHI-SQ CODE IS DATA constant covariate vector Cox model density function dummy variable duration dependency event history analysis event history data event oriented data exponential distribution exponential model extreme value distribution Figure gamma given Gompertz distribution Gompertz model Hamerle hazard function hazard rate hypothesis individual interval job change Kaplan-Meier estimator labor force experience LIFEREG likelihood function linear log likelihood function log-linear log-logistic distribution log-logistic model logarithm M3 EQ maximum likelihood estimation model without covariates months multiepisode multistate number of previously obtained P-VALUE P3RFUN parametric models partial likelihood period plotted PRES previously held jobs Program Example proportional hazards model regression model residuals sample Section significant SPSS STANDARD STATUS IS CEN subepisodes survivor function table method test statistic tion TRANSFORM transition UNIT IS 30 unobserved heterogeneity VARIABLE NAMES Weibull distribution Weibull model x2 value
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