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Parametric bootstrap under model mis-specification

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

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.

Suggested Citation

  • Lu, H.Y. Kevin & Young, G. Alastair, 2012. "Parametric bootstrap under model mis-specification," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2410-2420.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:8:p:2410-2420
    DOI: 10.1016/j.csda.2012.01.018
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    References listed on IDEAS

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    1. Lee, Stephen M.S. & Young, G. Alastair, 2005. "Parametric bootstrapping with nuisance parameters," Statistics & Probability Letters, Elsevier, vol. 71(2), pages 143-153, February.
    2. Stephen M. S. Lee, 2003. "Prepivoting by weighted bootstrap iteration," Biometrika, Biometrika Trust, vol. 90(2), pages 393-410, June.
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    Cited by:

    1. Guastadisegni, Lucia & Cagnone, Silvia & Moustaki, Irini & Vasdekis, Vassilis, 2022. "Use of the Lagrange multiplier test for assessing measurement invariance under model misspecification," LSE Research Online Documents on Economics 110358, London School of Economics and Political Science, LSE Library.

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