Nonparametric Estimation of Distributions in Random Effects Models

Jeffrey D. Hart, Isabel Cañette

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We propose using minimum distance to obtain nonparametric estimates of the distributions of components in random effects models. A main setting considered is equivalent to having a large number of small datasets whose locations, and perhaps scales, vary randomly, but which otherwise have a common distribution. Interest focuses on estimating the distribution that is common to all datasets, knowledge of which is crucial in multiple testing problems where a location/scale invariant test is applied to every small dataset. A detailed algorithm for computing minimum distance estimates is proposed, and the usefulness of our methodology is illustrated by a simulation study and an analysis of microarray data. Supplemental materials for the article, including R-code and a dataset, are available online. © 2011 American Statistical Association.
Original languageEnglish (US)
Pages (from-to)461-478
Number of pages18
JournalJournal of Computational and Graphical Statistics
Volume20
Issue number2
DOIs
StatePublished - Jan 2011
Externally publishedYes

Fingerprint Dive into the research topics of 'Nonparametric Estimation of Distributions in Random Effects Models'. Together they form a unique fingerprint.

Cite this