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    Nonparametric Estimation of Distributions in Random Effects Models

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    Type
    Article
    Authors
    Hart, Jeffrey D.
    Cañette, Isabel
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2011-01
    Permanent link to this record
    http://hdl.handle.net/10754/598999
    
    Metadata
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    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.
    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).
    Publisher
    Informa UK Limited
    Journal
    Journal of Computational and Graphical Statistics
    DOI
    10.1198/jcgs.2011.09121
    ae974a485f413a2113503eed53cd6c53
    10.1198/jcgs.2011.09121
    Scopus Count
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