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

Brian S. Caffo, Dongmei Liu, Robert B. Scharpf, Giovanni Parmigiani

Research output: Contribution to journalArticle


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.

Original languageEnglish (US)
Article number18
JournalInternational Journal of Biostatistics
Issue number1
StatePublished - Aug 10 2009



  • EM
  • Gene-expression
  • Hierarchical models
  • Microarray
  • Multilevel models

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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