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May 2012 Estimation in Discrete Parameter Models
Christine Choirat, Raffaello Seri
Statist. Sci. 27(2): 278-293 (May 2012). DOI: 10.1214/11-STS371


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.


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Christine Choirat. Raffaello Seri. "Estimation in Discrete Parameter Models." Statist. Sci. 27 (2) 278 - 293, May 2012.


Published: May 2012
First available in Project Euclid: 19 June 2012

zbMATH: 1330.62306
MathSciNet: MR2963996
Digital Object Identifier: 10.1214/11-STS371

Keywords: Detection , Discrete parameter space , efficiency , information inequalities , large deviations , superefficiency

Rights: Copyright © 2012 Institute of Mathematical Statistics


Vol.27 • No. 2 • May 2012
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