Nonparametric Estimation of Distributions in Random Effects Models Academic Article

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.

authors

• Hart, Jeffrey

published proceedings

• JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

author list (cited authors)

• Hart, J. D., & Canette, I.

citation count

• 3

complete list of authors

• Hart, Jeffrey D||Canette, Isabel

publication date

• January 2011

publisher

• Taylor & Francis  Publisher

published in

• Journal of Computational and Graphical Statistics  Journal

keywords

• Characteristic Function
• Identifiability
• Minimum Distance Estimation
• Quantile Function

Digital Object Identifier (DOI)

• 10.1198/jcgs.2011.09121

start page

• 461

end page

• 478

volume

• 20

issue

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1198%2Fjcgs.2011.09121