Nonparametric Estimation of Distributions in Random Effects Models Academic Article uri icon

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.

published proceedings

  • Journal of Computational and Graphical Statistics

author list (cited authors)

  • Hart, J. D., & Caette, I.

citation count

  • 3

complete list of authors

  • Hart, Jeffrey D||CaƱette, Isabel

publication date

  • January 2011