Event History Modeling: A Guide for Social Scientists
Cambridge University Press, Mar 29, 2004 - Political Science - 218 pages
Here is an accessible, up-to-date guide to event history analysis for researchers and advanced students in the social sciences. The foundational principles of event history analysis are discussed and ample examples are estimated and interpreted using standard statistical packages, such as STATA and S-Plus. Recent and critical innovations in diagnostics are discussed, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. The treatment of complicated events includes coverage of unobserved heterogeneity, repeated events, and competing risks models. The authors point out common problems in the analysis of time-to-event data in the social sciences and make recommendations regarding the implementation of duration modeling methods.
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Event History and Social Science
The Logic of Event History Analysis
Parametric Models for SingleSpell Duration Data
The Cox Proportional Hazards Model
Models for Discrete Data
Issues in Model Selection
Inclusion of TimeVarying Covariates
Diagnostic Methods for the Event History Model
application approach assumed assumption baseline hazard rate binary cabinet duration censored Collett competing risks model consider counting process covariates Cox model Cox-Snell residuals data set decreasing denotes dependent variable deviance residuals discrete-time discrete-time models discussed in Chapter distribution function duration data duration dependency duration models dyad Estimate s.e. event history analysis event history data event history models event occurs exact discrete example exponential distribution failure frailty model gamma gamma distribution given hazard function heterogeneity illustrate incumbent integrated hazard interest interval issue log-likelihood log-logistic logit model lowess martingale residuals maximum likelihood methods modeling strategies models discussed multilevel model multiple events observations parameter estimates parametric models partial likelihood function proportional hazards model ratio repeated events residual plots right-censored risk set S-Plus score residuals shape parameter social science social scientists spline split-population statistical substantive survival survivor function Table tion TVCs variance Weibull distribution Weibull model
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