WIAS Preprint No. 1776, (2013)

Critical dimension in profile semiparametric estimation



Authors

  • Andresen, Andreas
  • Spokoiny, Vladimir
    ORCID: 0000-0002-2040-3427

2010 Mathematics Subject Classification

  • 62F10 62J12 62F25 62H12

Keywords

  • maximum likelihood, local quadratic bracketing, spread, local concentration

Abstract

This paper revisits the classical inference results for profile quasi maximum likelihood estimators (profile MLE) in the semiparametric estimation problem. We mainly focus on two prominent theorems: the Wilks phenomenon and Fisher expansion for the profile MLE are stated in a new fashion allowing finite samples and model misspecification. The method of study is also essentially different from the usual analysis of the semiparametric problem based on the notion of the hardest parametric submodel. Instead we apply the local bracketing and the upper function devices from Spokoiny (2011). This novel approach particularly allows to address the important issue of the effective target and nuisance dimension and it does not involve any pilot estimator of the target parameter. The obtained nonasymptotic results are surprisingly sharp and yield the classical asymptotic statements including the asymptotic normality and efficiency of the profile MLE. The general results are specified to the important special cases of an i.i.d. sample.

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