Publications [#258271] of Alan E. Gelfand
search .Papers Published
- Gelfand, AE; Carlin, B. "Meximum likelihood estimation for constrained or missing data models." Canadian Journal of Statistics 21.3 (1993): 303-311. [doi]
(last updated on 2023/06/01)Abstract:
In statistical models involving constrained or missing data, likelihoods containing integrals emerge. In the case of both constrained and missing data, the result is a ratio of integrals, which for multivariate data may defy exact or approximate analytic expression. Seeking maximum‐likelihood estimates in such settings, we propose Monte Carlo approximants for these integrals, and subsequently maximize the resulting approximate likelihood. Iteration of this strategy expedites the maximization, while the Gibbs sampler is useful for the required Monte Carlo generation. As a result, we handle a class of models broader than the customary EM setting without using an EM‐type algorithm. Implementation of the methodology is illustrated in two numerical examples. Copyright © 1993 Statistical Society of Canada