Advertisement

Minimum Distance Estimation

  • Yu. Kutoyants
Part of the Mathematics and Its Applications book series (MAIA, volume 300)

Abstract

We introduce a new class of estimators — minimum distance estimators — and describe their properties in regular and nonstandard situations. These estimators, in the regular case of Hilbert metrics, are consistent and asymptotically normal. In nonstandard situations, their behavior is similar to the behavior of the MLE. We find that in certain circumstances these estimators are local asymptotic minimax (asymptotically optimal) and they are better than the MLE and BE. In the case of L1-norm and sup-norm the limit distributions of the estimators are non-Gaussian, but the limit (as T→ 0) distributions of these (limit) as ε→ 0 random variables are Gaussian.

Keywords

Function Versus Gaussian Process Wiener Process Asymptotic Normality Regular Case 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media Dordrecht 1994

Authors and Affiliations

  • Yu. Kutoyants
    • 1
  1. 1.Département de MathématiquesFaculté des Sciences, Université du MaineLe MansFrance

Personalised recommendations