Efficiency of Monte Carlo EM and Simulated Maximum Likelihood in Two-Stage Hierarchical Models
- 1 March 2003
- journal article
- Published by Taylor & Francis in Journal of Computational and Graphical Statistics
- Vol. 12 (1) , 214-229
- https://doi.org/10.1198/1061860031338
Abstract
Likelihood estimation in hierarchical models is often complicated by the fact that the likelihood function involves an analytically intractable integral. Numerical approximation to this integral is...Keywords
This publication has 30 references indexed in Scilit:
- A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical modelStatistical Modelling, 2001
- Hierarchical Models: A Current Computational PerspectiveJournal of the American Statistical Association, 2000
- Misspecified heteroskedasticity in the panel probit model: A small sample comparison of GMM and SML estimatorsJournal of Econometrics, 2000
- Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM AlgorithmJournal of the Royal Statistical Society Series B: Statistical Methodology, 1999
- Methods for Approximating Integrals in Statistics with Special Emphasis on Bayesian Integration ProblemsStatistical Science, 1995
- Monte Carlo EM Estimation for Time Series Models Involving CountsJournal of the American Statistical Association, 1995
- Stochastic volatility in asset prices estimation with simulated maximum likelihoodJournal of Econometrics, 1994
- Maximum‐likelihood estimation for constrained‐ or missing‐data modelsThe Canadian Journal of Statistics / La Revue Canadienne de Statistique, 1993
- Approximate Inference in Generalized Linear Mixed ModelsJournal of the American Statistical Association, 1993
- Bayesian Inference in Econometric Models Using Monte Carlo IntegrationEconometrica, 1989