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Finite Population Estimators in Stochastic Search Variable Selection
| Content Provider | Semantic Scholar |
|---|---|
| Author | Clyde, Merlise A. Ghosh, Joyee |
| Copyright Year | 2011 |
| Abstract | Monte Carlo algorithms are commonly used to identify a set of models for Bayesian model selection or model averaging. Because empirical frequencies of models are often zero or one in high dimensional problems, posterior probabilities calculated from the observed marginal likelihoods, re-normalized over the sampled models are often employed. Such estimates are the only recourse in several newer stochastic search algorithms. In this paper, we prove that renormalization of posterior probabilities over the set of sampled models generally leads to bias which may dominate mean squared error. Viewing the model space as a finite population, we propose a new estimator based on a ratio of Horvitz-Thompson estimators which incorporates observed marginal likelihoods, but is approximately unbiased. This is shown to lead to a reduction in mean squared error compared to the empirical or re-normalized estimators, with little increase in computational costs. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://dukespace.lib.duke.edu/dspace/bitstream/handle/10161/11723/Biometrika-2012-Clyde-981-8.pdf?sequence=1 |
| Alternate Webpage(s) | http://ftp.stat.duke.edu/WorkingPapers/10-11.pdf |
| Alternate Webpage(s) | http://ftp.isds.duke.edu/WorkingPapers/10-11.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Arabic numeral 0 Bayes factor Bayesian network Estimated Feature selection Genetic Selection Leucaena pulverulenta Marginal model Mean squared error Model selection Monte Carlo algorithm Probability Sampling - Surgical action Search algorithm Stochastic optimization |
| Content Type | Text |
| Resource Type | Article |
