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M-Estimators

  • Aad W. van der Vaart
  • Jon A. Wellner
Part of the Springer Series in Statistics book series (SSS)

Abstract

The most important method of constructing statistical estimators is to choose the estimator to maximize a certain criterion function. We shall call such estimators M-estimators (from “maximum” or “minimum”). In the case of i.i.d. observations X 1,..., X n , a common type of criterion function is of the form
$$\theta \mapsto {{\text{P}}_n}{m_\theta } = \frac{1}{n}\sum\limits_{i = 1}^n {{m_\theta }} ({X_i}),$$
for known given functions m θ on the sample space. In particular, the method of maximum likelihood estimation corresponds to the choice m θ = log p θ , where p θ is the density of the observations.† The theory of empirical processes comes in naturally when studying the asymptotic properties of these estimators. In this chapter we present several results that give the asymptotic distribution of M-estimators. Some results are of a general nature, while others presume the set-up of i.i.d. observations.

Keywords

Maximum Likelihood Estimator Outer Probability Statistical Application Criterion Function Asymptotic Normality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Aad W. van der Vaart
    • 1
  • Jon A. Wellner
    • 2
  1. 1.Department of Mathematics and Computer ScienceFree UniversityAmsterdamThe Netherlands
  2. 2.StatisticsUniversity of WashingtonSeattleUSA

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