The Optimal Confidence Region for a Random Parameter

Hajime Uno, Lu Tian and L.J. Wei
Additional contact information
Hajime Uno: Harvard University
Lu Tian: Northwestern University
L.J. Wei: Harvard University

No 1013, Harvard University Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: Under a two-level hierarchical model, suppose that the distribution of the random parameter is known or can be estimated well. Data are generated via a fixed, but unobservable realization of this parameter. In this paper, we derive the smallest confidence region of the random parameter under a joint Bayesian/frequentist paradigm. On average this optimal region can be much smaller than the corresponding Bayesian highest posterior density region. The new estimation procedure is appealing when one deals with data generated under a highly parallel structure, for example, data from a trial with a large number of clinical centers involved or genome-wide gene-expession data for estimating individual gene- or center-specific parameters simultaneously. The new proposal is illustrated with a typical microarray data set and its performance is examined via a small simulation study.

Keywords: Empirical Bayes; Gene-expession; Global Clinical Trials; Hierarchical Model; Highest Posterior Density Region (search for similar items in EconPapers)
Date: 2004-07-22
Note: oai:bepress.com:harvardbiostat-1013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.bepress.com/cgi/viewcontent.cgi?article=1013&context=harvardbiostat (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bep:hvdbio:1013

Access Statistics for this paper

More papers in Harvard University Biostatistics Working Paper Series from Berkeley Electronic Press
Bibliographic data for series maintained by Christopher F. Baum ().