Estimation in Discrete Parameter Models

Abstract

In some estimation problems, especially in applications dealing with information theory, signal processing and biology, theory provides us with additional information allowing us to restrict the parameter space to a finite number of points. In this case, we speak of discrete parameter models. Even though the problem is quite old and has interesting connections with testing and model selection, asymptotic theory for these models has hardly ever been studied. Therefore, we discuss consistency, asymptotic distribution theory, information inequalities and their relations with efficiency and superefficiency for a general class of $m$-estimators.

Date
Apr 29, 2002 — May 3, 2002
Event
Colloque Jeunes Probabilistes et Statisticiens, Aussois 2002
Location
Aussois, France
Raffaello Seri
Professor of Econometrics

My research interests include statistics, numerical analysis, operations research, psychology, economics and management.