Dynamic Mixture-of-Experts Models for Longitudinal and Discrete-Time Survival Data

Riksbank Research Paper Series No. 99

Sveriges Riksbank Working Paper Series No. 268

39 Pages Posted: 14 Aug 2013

See all articles by Matias Quiroz

Matias Quiroz

Sveriges Riksbank - Research Division; Stockholm University - Department of Statistics

Mattias Villani

Linkoping University

Date Written: May 2013

Abstract

We propose a general class of flexible models for longitudinal data with special emphasis on discrete-time survival data. The model is a finite mixture model where the subjects are allowed to move between components through time. The time-varying probabilities of component memberships is modeled as a function of subject-specific time-varying covariates. This allows for interesting within-subject dynamics and manageable computations even with a large number of subjects. Each parameter in the component densities and in the mixing function is connected to its own set of covariates through a link function. The models are estimated using a Bayesian approach via a highly efficient Markov Chain Monte Carlo (MCMC) algorithm with tailored proposals and variable selection in all set of covariates. The focus of the paper is on models for discrete-time survival data with an application to bankruptcy prediction for Swedish firms, using both exponential and Weibull mixture components. The dynamic mixture-of-experts models are shown to have an interesting interpretation and to dramatically improve the out-of-sample predictive density forecasts compared to models with time-invariant mixture probabilities.

Keywords: Bayesian inference, Markov Chain Monte Carlo, Bayesian variable selection, Survival Analysis, Mixture-of-experts

JEL Classification: C11, C41, D21, G33

Suggested Citation

Quiroz, Matias and Villani, Mattias, Dynamic Mixture-of-Experts Models for Longitudinal and Discrete-Time Survival Data (May 2013). Riksbank Research Paper Series No. 99. Available at SSRN: https://ssrn.com/abstract=2285450 or http://dx.doi.org/10.2139/ssrn.2285450

Matias Quiroz (Contact Author)

Sveriges Riksbank - Research Division ( email )

S-103 37 Stockholm
Sweden

Stockholm University - Department of Statistics ( email )

Stockholm, SE-106 91
Sweden

Mattias Villani

Linkoping University ( email )

Överstegatan 30
Linkoping, 581 83
Sweden

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