Likelihood Estimation of Conjugacy Relationships in Linear Models with Applications to High-Throughput Genomics

Caffo Brian S, Liu Dongmei, Scharpf Robert B. and Parmigiani Giovanni
Additional contact information
Caffo Brian S: Johns Hopkins University
Liu Dongmei: London School of Hygiene & Tropical Medicine
Scharpf Robert B.: Johns Hopkins University
Parmigiani Giovanni: Johns Hopkins University

The International Journal of Biostatistics, 2009, vol. 5, issue 1, 25

Abstract: In the simultaneous estimation of a large number of related quantities, multilevel models provide a formal mechanism for efficiently making use of the ensemble of information for deriving individual estimates. In this article we investigate the ability of the likelihood to identify the relationship between signal and noise in multilevel linear mixed models. Specifically, we consider the ability of the likelihood to diagnose conjugacy or independence between the signals and noises. Our work was motivated by the analysis of data from high-throughput experiments in genomics. The proposed model leads to a more flexible family. However, we further demonstrate that adequately capitalizing on the benefits of a well fitting fully-specified likelihood in the terms of gene ranking is difficult.

Keywords: multilevel models; hierarchical models; EM; microarray; gene-expression (search for similar items in EconPapers)
Date: 2009
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DOI: 10.2202/1557-4679.1129

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