Stacked Laplace-EM algorithm for duration models with time-varying and random effects

Kauermann G, Xu R, Vaida F (2008)
COMPUTATIONAL STATISTICS & DATA ANALYSIS 52(5): 2514-2528.

Journal Article | Published | English

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Abstract
An extension of the Cox proportional hazards model for clustered survival data is proposed. This allows both general random effects (frailties) and time-varying regression coefficients, the latter being smooth functions of time. The model is fitted using a mixed-model representation of penalized spline smoothing which offers a unified framework for estimation of the baseline hazard, the smooth effects and the random effects. The estimator is computed using a stacked laplace-EM (SLaEM) algorithm. More specifically, the smoothing parameters are integrated out in the log likelihood via a Laplace approximation. The approximation itself involves an integrated log-likelihood over the random cluster effects, for which the EM algorithm is used. A marginal Akaike information criterion is developed for selection among possible candidate models. The time-varying and mixed effects model is applied to unemployment data taken from the German Socio-Economic Panel. The duration of unemployment is modeled in a flexible way including smooth covariate effects and individual random effects. (c) 2007 Elsevier B.V. All rights reserved.
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Kauermann G, Xu R, Vaida F. Stacked Laplace-EM algorithm for duration models with time-varying and random effects. COMPUTATIONAL STATISTICS & DATA ANALYSIS. 2008;52(5):2514-2528.
Kauermann, G., Xu, R., & Vaida, F. (2008). Stacked Laplace-EM algorithm for duration models with time-varying and random effects. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 52(5), 2514-2528.
Kauermann, G., Xu, R., and Vaida, F. (2008). Stacked Laplace-EM algorithm for duration models with time-varying and random effects. COMPUTATIONAL STATISTICS & DATA ANALYSIS 52, 2514-2528.
Kauermann, G., Xu, R., & Vaida, F., 2008. Stacked Laplace-EM algorithm for duration models with time-varying and random effects. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 52(5), p 2514-2528.
G. Kauermann, R. Xu, and F. Vaida, “Stacked Laplace-EM algorithm for duration models with time-varying and random effects”, COMPUTATIONAL STATISTICS & DATA ANALYSIS, vol. 52, 2008, pp. 2514-2528.
Kauermann, G., Xu, R., Vaida, F.: Stacked Laplace-EM algorithm for duration models with time-varying and random effects. COMPUTATIONAL STATISTICS & DATA ANALYSIS. 52, 2514-2528 (2008).
Kauermann, Göran, Xu, Ronghui, and Vaida, Florin. “Stacked Laplace-EM algorithm for duration models with time-varying and random effects”. COMPUTATIONAL STATISTICS & DATA ANALYSIS 52.5 (2008): 2514-2528.
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