Power Conjugate Multilevel Models with Applications to Genomics

This file is part of IDEAS, which uses RePEc data

Power Conjugate Multilevel Models with Applications to Genomics

Author Info

Brian Caffo (The Johns Hopkins Bloomberg School of Public Health)
Liu Dongmei (Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics)
Giovanni Parmigiani (The Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University & Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health)

Abstract

In the simultaneous estimation of a large number of related quantities, multilevel models provide a formal mechanism for effciently making use of the ensemble of information for deriving individual estimates. In this article we present a novel and flexible class of normal multilevel models, referred to as the "power conjugate family". This family overcomes some of the severe restrictions posed by standard conjugate normal models in describing the relationship between sources of variations at different levels of the model, while retaining attractive properties from the point of view of computations. We show that estimates based on this generalized family of conjugate distributions, outperform currently prevalent methods in a range of plausible simulated experiments. Our work was motivated by the analysis of data from high-throughput experiments in genomics. We illustrate the use of the power conjugate family on two such data sets, one of which gives an example where uncritical application of standard conjugate models can produce poor results.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

Publisher Info
Paper provided by Berkeley Electronic Press in its series Johns Hopkins University Dept. of Biostatistics Working Paper Series with number 1062.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 06 Oct 2004
Date of revision:
Handle: RePEc:bep:jhubio:1062

Note: oai:bepress.com:jhubiostat-1062
Contact details of provider:
Web page: http://www.bepress.com

For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).

Related research
Keywords:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

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. [Downloadable!] (restricted)
Gordon K. Smyth, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, Berkeley Electronic Press, vol. 3(1). [Downloadable!]
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. [Downloadable!] (restricted)

Full references

Statistics
Access and download statistics

Did you know? There are over 21000 authors registered on RePEc Author Service.

This page was last updated on 2009-12-15.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.