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    Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection bias

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    Type
    Article
    Authors
    Ma, Yanyuan
    Kim, Mijeong
    Genton, Marc G. cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2013-09
    Permanent link to this record
    http://hdl.handle.net/10754/562952
    
    Metadata
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    Abstract
    We propose semiparametric methods to estimate the center and shape of a symmetric population when a representative sample of the population is unavailable due to selection bias. We allow an arbitrary sample selection mechanism determined by the data collection procedure, and we do not impose any parametric form on the population distribution. Under this general framework, we construct a family of consistent estimators of the center that is robust to population model misspecification, and we identify the efficient member that reaches the minimum possible estimation variance. The asymptotic properties and finite sample performance of the estimation and inference procedures are illustrated through theoretical analysis and simulations. A data example is also provided to illustrate the usefulness of the methods in practice. © 2013 American Statistical Association.
    Sponsors
    This research was partially supported by NSF grants DMS-0906341, DMS-1007504, and DMS-1100492; NINDS grant R01-NS073671; 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 the American Statistical Association
    DOI
    10.1080/01621459.2013.816184
    ae974a485f413a2113503eed53cd6c53
    10.1080/01621459.2013.816184
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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