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A Mixture Model Approach to Empirical Bayes Testing and Estimation

Omkar Muralidharan

出版	Stanford University, 2011
ISBN	STANFORD:pp730hw1567
URL	http://books.google.com.hk/books?id=9EiFxoz1y-gC&hl=&source=gbs_api
EBook	SAMPLE

註釋

Many modern statistical problems require making similar decisions or estimates for many different entities. For example, we may ask whether each of 10,000 genes is associated with some disease, or try to measure the degree to which each is associated with the disease. As in this example, the entities can often be divided into a vast majority of "null" objects and a small minority of interesting ones. Empirical Bayes is a useful technique for such situations, but finding the right empirical Bayes method for each problem can be difficult. Mixture models, however, provide an easy and effective way to apply empirical Bayes. This thesis motivates mixture models by analyzing a simple high-dimensional problem, and shows their practical use by applying them to detecting single nucleotide polymorphisms.