Model selection in Bayesian method
Date Issued
2010
Date
2010
Author(s)
Chang, Fu-Chen
Abstract
This paper is concerned with the problem of model selection among multiple populations. Here we use a nonparametric approach. We would like to find a decision rule to effectively identify which population each data comes from. We create a decision rule, based on Bayesian theorem, called Bayesian discriminant rule. Furthermore, we construct two estimated methods to decide the decision rule – density estimation and logistic regression. An unknown density function has to be estimated in the decision rule. Now it is vital to find an accurate way to estimate this density function or logistic probability to arise the classification rate. By using bandwidth selection for local likelihood density estimation or local logistic regression that minimizes AIC criterion does improve the results of model selection. A small simulation shows that for a large enough sample size, the method performs well.
Subjects
model selection
Bayesian discriminant rule
Local likelihood density estimation
local logistic regression
classification
Bandwidth selection
AIC
Type
thesis
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