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    Local and omnibus goodness-of-fit tests in classical measurement error models

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    Type
    Article
    Authors
    Ma, Yanyuan
    Hart, Jeffrey D.
    Janicki, Ryan
    Carroll, Raymond J.
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2010-09-14
    Online Publication Date
    2010-09-14
    Print Publication Date
    2011-01
    Permanent link to this record
    http://hdl.handle.net/10754/598222
    
    Metadata
    Show full item record
    Abstract
    We consider functional measurement error models, i.e. models where covariates are measured with error and yet no distributional assumptions are made about the mismeasured variable. We propose and study a score-type local test and an orthogonal series-based, omnibus goodness-of-fit test in this context, where no likelihood function is available or calculated-i.e. all the tests are proposed in the semiparametric model framework. We demonstrate that our tests have optimality properties and computational advantages that are similar to those of the classical score tests in the parametric model framework. The test procedures are applicable to several semiparametric extensions of measurement error models, including when the measurement error distribution is estimated non-parametrically as well as for generalized partially linear models. The performance of the local score-type and omnibus goodness-of-fit tests is demonstrated through simulation studies and analysis of a nutrition data set.
    Citation
    Ma Y, Hart JD, Janicki R, Carroll RJ (2010) Local and omnibus goodness-of-fit tests in classical measurement error models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73: 81–98. Available: http://dx.doi.org/10.1111/j.1467-9868.2010.00751.x.
    Sponsors
    Carroll and Ma's research was supported by a grant from the National Cancer Institute (CA57030). Carroll and Hart's work was also supported by award KUS-C1-016-04, made by King Abdullah University of Science and Technology. Ma's work is supported by National Science Foundation grant DMS-0906341, and Hart's was partially supported by National Science Foundation grant DMS-0604801. Janicki's work was done while at the University of Maryland. He thanks his adviser Professor Abram Kagan for his advice and support.
    Publisher
    Wiley
    Journal
    Journal of the Royal Statistical Society: Series B (Statistical Methodology)
    DOI
    10.1111/j.1467-9868.2010.00751.x
    PubMed ID
    21339886
    PubMed Central ID
    PMC3040518
    ae974a485f413a2113503eed53cd6c53
    10.1111/j.1467-9868.2010.00751.x
    Scopus Count
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