Difficulties in Drawing Inferences With Finite-Mixture Models
| Autor: | Joseph L Schafer, Hwan Chung, Eric Loken |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Statistics and Probability
Mathematical optimization General Mathematics Univariate Sample (statistics) Markov chain Monte Carlo Parameter space Bayesian inference symbols.namesake Label switching Expectation–maximization algorithm symbols A priori and a posteriori Statistics Probability and Uncertainty Mathematics |
| Zdroj: | The American Statistician. 58:152-158 |
| ISSN: | 1537-2731 0003-1305 |
| DOI: | 10.1198/0003130043286 |
| Popis: | Likelihood functions from finite mixture models have many unusual features. Maximum likelihood (ML) estimates may behave poorly over repeated samples, and the abnormal shape of the likelihood often makes it difficult to assess the uncertainty in parameter estimates. Bayesian inference via Markov chain Monte Carlo (MCMC) can be a useful alternative to ML, but the component labels may switch during the MCMC run, making the output difficult to interpret. Two basic methods for handling the label-switching problem have been proposed: imposing constraints on the parameter space and cluster-based relabeling of the simulated parameters. We have found that label switching may also be reduced by supplying small amounts of prior information that are asymmetric with respect to the mixture components. Simply assigning one observation to each component a priori may effectively eliminate the problem. Using a very simple example—a univariate sample from a mixture of two exponentials—we evaluate the performance of likelih... |
| Databáze: | OpenAIRE |
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