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Asymptotic Properties of Adaptive Penalized Optimal Designs over a Finite Space

  • Luc Pronzato
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

Adaptive optimal design with a cost constraint is considered, both for LS estimation in nonlinear regression and ML estimation in Bernoulli-type experiments, with possible applications in clinical trials. We obtain the strong consistency of the estimators for designs over a finite space, both when the cost level is fixed (and the adaptive design converges to an optimum constrained design) and when the objective is to minimize the cost. Moreover, the asymptotic normality of the estimators is obtained in the first situation, with an asymptotic covariance matrix given by the inverse of the usual information matrix, calculated as if the design were not constructed sequentially.

Keywords

Optimal Design Asymptotic Property Design Point Asymptotic Normality Strong Consistency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Laboratoire I3SCNRS/Université de Nice–Sophia AntipolisNiceFrance

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