Bayesian estimation of transition probabilities from repeated cross sections

Ben Pelzer and Rob Eisinga

Statistica Neerlandica, 2002, vol. 56, issue 1, 23-33

Abstract: This paper discusses some simple practical advantages of Markov chain Monte Carlo (MCMC) methods in estimating entry and exit transition probabilities from repeated independent surveys. Simulated data are used to illustrate the usefulness of MCMC methods when the likelihood function has multiple local maxima. Actual data on the evaluation of an HIV prevention intervention program among drug users are used to demonstrate the advantage of using prior information to enhance parameter identificaiton. The latter example also demonstrates an important strength of the MCMC approach, namely the ability to make inferences on arbitrary functions of model parameters.

Date: 2002
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