WIAS Preprint No. 1992, (2014)

Bootstrap confidence sets under a model misspecification



Authors

  • Spokoiny, Vladimir
    ORCID: 0000-0002-2040-3427
  • Zhilova, Mayya

2010 Mathematics Subject Classification

  • 62F25 62F40 62E17

2008 Physics and Astronomy Classification Scheme

  • 02.50.Tt 02.50.Ng

Keywords

  • likelihood-based confidence set, misspecified model, finite sample size, multiplier bootstrap

Abstract

A multiplier bootstrap procedure for construction of likelihood-based confidence sets is considered for finite samples and possible model misspecification. Theoretical results justify the bootstrap consistency for small or moderate sample size and allow to control the impact of the parameter dimension: the bootstrap approximation works if the ratio of cube of the parameter dimension to the sample size is small. The main result about bootstrap consistency continues to apply even if the underlying parametric model is misspecified under the so called Small Modeling Bias condition. In the case when the true model deviates significantly from the considered parametric family, the bootstrap procedure is still applicable but it becomes a bit conservative: the size of the constructed confidence sets is increased by the modeling bias. We illustrate the results with numerical examples of misspecified constant and logistic regressions.

Appeared in

  • Ann. Statist., 43 (2015), pp. 2653--2675.

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