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Biometrika 1994 81(2):317-330; doi:10.1093/biomet/81.2.317
© 1994 by Biometrika Trust
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Dynamic modelling and penalized likelihood estimation for discrete time survival data

LUDWIG FAHRMEIR

Seminar für Statistik, Universität München Ludwigstrasse 33, D-80539 München, Germany

SUMMMARY: This paper describes a dynamic or state-space approach for analyzing discrete time or grouped survival data. Simultaneous estimation of baseline hazard functions and of time-varying covariate effects is based on maximization of posterior densities or, equivalently, a penalized likelihood, leading to Kalman-type smoothing algorithms. Data-driven choice of unknown smoothing parameters is possible via an EM-type procedure. The methods are illustrated by applications to real data.

Key Words: Dynamic model • Grouped survival data • Hazard function • Penalized likelihood • Posterior mode smoothing • Time-varying effects


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