Nonparametric Estimation of Distributions in Random Effects Models

Handle URI:
http://hdl.handle.net/10754/598999
Title:
Nonparametric Estimation of Distributions in Random Effects Models
Authors:
Hart, Jeffrey D.; Cañette, Isabel
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.
Citation:
Hart 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.
Publisher:
Informa UK Limited
Journal:
Journal of Computational and Graphical Statistics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Jan-2011
DOI:
10.1198/jcgs.2011.09121
Type:
Article
ISSN:
1061-8600; 1537-2715
Sponsors:
The 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).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorHart, Jeffrey D.en
dc.contributor.authorCañette, Isabelen
dc.date.accessioned2016-02-25T13:50:54Zen
dc.date.available2016-02-25T13:50:54Zen
dc.date.issued2011-01en
dc.identifier.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.en
dc.identifier.issn1061-8600en
dc.identifier.issn1537-2715en
dc.identifier.doi10.1198/jcgs.2011.09121en
dc.identifier.urihttp://hdl.handle.net/10754/598999en
dc.description.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.en
dc.description.sponsorshipThe 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).en
dc.publisherInforma UK Limiteden
dc.subjectCharacteristic functionen
dc.subjectIdentifiabilityen
dc.subjectMinimum distance estimationen
dc.subjectQuantile functionen
dc.titleNonparametric Estimation of Distributions in Random Effects Modelsen
dc.typeArticleen
dc.identifier.journalJournal of Computational and Graphical Statisticsen
dc.contributor.institutionTexas A and MUniversity, TX, 77843, United Statesen
dc.contributor.institutionStataCorp, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
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