Optimal experimental design criterion for discriminating semiparametric models

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper studies the optimal experimental design problem to discriminate two regression models. Recently, López-Fidalgo et al. [2007. An optimal experimental design criterion for discriminating between non-normal models. J. Roy. Statist. Soc. B 69, 231-242] extended the conventional T-optimality criterion by Atkinson and Fedorov [1975a. The designs of experiments for discriminating between two rival models. Biometrika 62, 57-70; 1975b. Optimal design: experiments for discriminating between several models. Biometrika 62, 289-303] to deal with non-normal parametric regression models, and proposed a new optimal experimental design criterion based on the Kullback-Leibler information divergence. In this paper, we extend their parametric optimality criterion to a semiparametric setup, where we only need to specify some moment conditions for the null or alternative regression model. Our criteria, called the semiparametric Kullback-Leibler optimality criteria, can be implemented by applying a convex duality result of partially finite convex programming. The proposed method is illustrated by a simple numerical example.

Original languageEnglish
Pages (from-to)4141-4150
Number of pages10
JournalJournal of Statistical Planning and Inference
Volume138
Issue number12
DOIs
Publication statusPublished - 2008 Dec 1
Externally publishedYes

Fingerprint

Optimal Experimental Design
Semiparametric Model
Optimality Criteria
Design of experiments
Regression Model
Convex Duality
Kullback-Leibler Information
Parametric Regression
Moment Conditions
Convex Programming
Design of Experiments
Parametric Model
Null
Divergence
Model
Numerical Examples
Convex optimization
Alternatives
Experiment
Experimental design

Keywords

  • Convex duality
  • Optimal experimental design
  • Semiparametric model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

Optimal experimental design criterion for discriminating semiparametric models. / Otsu, Taisuke.

In: Journal of Statistical Planning and Inference, Vol. 138, No. 12, 01.12.2008, p. 4141-4150.

Research output: Contribution to journalArticle

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