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Parametric bootstrap under model mis-specification

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  • Lu, H.Y. Kevin
  • Young, G. Alastair
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    Abstract

    Under model correctness, highly accurate inference on a scalar interest parameter in the presence of a nuisance parameter can be achieved by several routes, among them considering the bootstrap distribution of the signed root likelihood ratio statistic. The context of model mis-specification is considered and inference based on a robust form of the signed root statistic is discussed in detail. Stability of the distribution of the statistic allows accurate inference, outperforming that based on first-order asymptotic approximation, by considering the bootstrap distribution of the statistic under the incorrectly assumed distribution. Comparisons of this simple approach with alternative analytic and non-parametric inference schemes are discussed.

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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 8 ()
    Pages: 2410-2420

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    Handle: RePEc:eee:csdana:v:56:y:2012:i:8:p:2410-2420

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Asymptotic approximation; Model mis-specification; Non-parametric inference; Parametric bootstrap; Resampling; Signed root likelihood ratio statistic;

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