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Parametric bootstrap under model mis-specification. (English) Zbl 1252.62034

Summary: 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 nonparametric inference schemes are discussed.

MSC:

62F40 Bootstrap, jackknife and other resampling methods
62G20 Asymptotic properties of nonparametric inference

Software:

LogConcDEAD
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References:

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