Minimum entropy estimation in semi parametric models

被引：0

作者：

Wolsztynski, K ^{[1
]}

Thierry, K ^{[1
]}

Pronzato, L ^{[1
]}

机构：

[1] Univ Nice, CNRS, F-06903 Sophia Antipolis, France

来源：

2004 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL II, PROCEEDINGS: SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING SIGNAL PROCESSING THEORY AND METHODS | 2004年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper is a continuation of the work initiated in [1, 2]: we estimate parameters in a regression model, linear or not, by minimizing (an estimate of) the entropy of the symmetrized residuals, obtained by a kernel estimation of their distribution. The objective is to obtain efficiency in the absence of knowledge of the density f of the observation errors, which is called adaptive estimation, see in particular [3, 4, 5] and the review paper [6]. Connections and differences with previous work are indicated. Numerical results illustrate that asymptotic efficiency is not necessarily in conflict with robustness.

引用

页码：1045 / 1048

页数：4

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