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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Caffo Brian S & Liu Dongmei & Scharpf Robert B. & Parmigiani Giovanni, 2009. "Likelihood Estimation of Conjugacy Relationships in Linear Models with Applications to High-Throughput Genomics," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-25, May.
    • Handle: RePEc:bpj:ijbist:v:5:y:2009:i:1:n:18
    DOI: 10.2202/1557-4679.1129
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    References listed on IDEAS

    as
    1. Giovanni Parmigiani & Elizabeth S. Garrett & Ramaswamy Anbazhagan & Edward Gabrielson, 2002. "A statistical framework for expression‐based molecular classification in cancer," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 717-736, October.
    2. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    3. Ibrahim J. G. & Chen M-H. & Gray R. J., 2002. "Bayesian Models for Gene Expression With DNA Microarray Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 88-99, March.
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