Abstract #300910

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JSM 2003 Abstract #300910
Activity Number: 63
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Computing
Abstract - #300910
Title: Monte Carlo State-Space Likelihoods by Weighted Posterior Kernel Density Estimation
Author(s): Perry deValpine*+
Companies: University of California, Berkeley
Address: Department of Integrative Biology, Berkeley, CA, 94720-3140,
Keywords: state-space ; importance sampling ; population dynamics ; MCMC ; kernel density estimation
Abstract:

Maximum likelihood estimation and likelihood ratio tests for nonlinear, non-Gaussian state-space models require numerical integration for likelihood calculations. Several methods, including Monte Carlo expectation maximization, Monte Carlo likelihood ratios, direct Monte Carlo integration, and Particle Filter likelihoods, are inefficient for our motivating problem of stage-structured population dynamics models in experimental settings. We present a Monte Carlo kernel likelihood (MCKL) method that estimates classical likelihoods up to a constant by weighted kernel density estimates of Bayesian posteriors, with weights derived using posteriors as importance sampling densities for unnormalized kernel smoothing integrals. Simulated examples show that MCKL is much more efficient than previous approaches for our motivating problem. To improve accuracy of MCKL maximum likelihood estimation, we discuss "zooming in" on maximum likelihood parameters using refined priors as well as estimating mode bias due to smoothing using posterior cumulants. Simulated examples show that these procedures can give accurate results in at least 20 parameter dimensions.

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