Nonparametric Estimation of Distributions in Random Effects Models
KAUST Grant NumberKUS-C1-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/598999
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AbstractWe 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.
CitationHart JD, Cañette I (2011) Nonparametric Estimation of Distributions in Random Effects Models. Journal of Computational and Graphical Statistics 20: 461–478. Available: http://dx.doi.org/10.1198/jcgs.2011.09121.
SponsorsThe work of Professor Hart was supported by NSF grant DMS-0604801 and by Award no. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
PublisherInforma UK Limited