- Statist. Sci.
- Volume 27, Number 2 (2012), 278-293.
Estimation in Discrete Parameter Models
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.
Statist. Sci., Volume 27, Number 2 (2012), 278-293.
First available in Project Euclid: 19 June 2012
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Choirat, Christine; Seri, Raffaello. Estimation in Discrete Parameter Models. Statist. Sci. 27 (2012), no. 2, 278--293. doi:10.1214/11-STS371. https://projecteuclid.org/euclid.ss/1340110873