Parametric bootstrap under model mis-specification

H.Y. Kevin Lu and G. Alastair Young

Computational Statistics & Data Analysis, 2012, vol. 56, issue 8, 2410-2420

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.

Keywords: Asymptotic approximation; Model mis-specification; Non-parametric inference; Parametric bootstrap; Resampling; Signed root likelihood ratio statistic (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:8:p:2410-2420

DOI: 10.1016/j.csda.2012.01.018

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