| Paper: | SPTM-P12.10 | ||
| Session: | Estimation | ||
| Time: | Friday, May 21, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, and Class. Thry & Apps. | ||
| Title: | MINIMUM ENTROPY ESTIMATION IN SEMI PARAMETRIC MODELS | ||
| Authors: | Éric Wolsztynski; UNSA - CNRS | ||
| Éric Thierry; UNSA - CNRS | |||
| Luc Pronzato; CNRS | |||
| Abstract: | 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 the distribution of the residuals. The objective is to obtain efficiency in the absence of knowledge of the density 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. | ||
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