In event history analysis, the problem of modeling two interdependent processes is still not completely solved. In a frequentist framework, there are two most general approaches: the causal approach and the system approach. The recent growing interest in Bayesian statistics suggests some interesting works on survival models and event history analysis in a Bayesian perspective. In this work we present a possible solution for the analysis of dynamic interdependence by a Bayesian perspective in a graphical duration model framework, using marked point processes. Main results from the Bayesian approach and the comparison with the frequentist one are illustrated on a real example: the analysis of the dynamic relationship between fertility and female employment.

A Bayesian approach to model interdependent event histories by graphical models / E.Dreassi; A.Gottard. - In: STATISTICAL METHODS & APPLICATIONS. - ISSN 1618-2510. - STAMPA. - 16 (1):(2007), pp. 39-49. [10.1007/s10260-006-0018-4]

A Bayesian approach to model interdependent event histories by graphical models

DREASSI, EMANUELA;GOTTARD, ANNA
2007

Abstract

In event history analysis, the problem of modeling two interdependent processes is still not completely solved. In a frequentist framework, there are two most general approaches: the causal approach and the system approach. The recent growing interest in Bayesian statistics suggests some interesting works on survival models and event history analysis in a Bayesian perspective. In this work we present a possible solution for the analysis of dynamic interdependence by a Bayesian perspective in a graphical duration model framework, using marked point processes. Main results from the Bayesian approach and the comparison with the frequentist one are illustrated on a real example: the analysis of the dynamic relationship between fertility and female employment.
2007
16 (1)
39
49
E.Dreassi; A.Gottard
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/334633
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