Local Optimality Criteria Based on Asymptotic Normality

  • Luc Pronzato
  • Andrej Pázman
Part of the Lecture Notes in Statistics book series (LNS, volume 212)


The approach considered in this chapter is probably the most common for designing experiments in nonlinear situations. It consists in optimizing a scalar function of the asymptotic covariance matrix of the estimator and thus relies on asymptotic normality, as considered in  Chaps. 3 and  4. Design based on more accurate characterizations of the precision of the estimation will be considered in  Chap. 6. Additionally to asymptotic normality, the approach also supposes that the asymptotic covariance matrix takes the form of the inverse of an information matrix, as it is the case, for instance, for weighted LS with optimum weights, see  Sect. 3.1.3, or maximum likelihood estimation, see  Sect. 4.2.


Design Criterion Asymptotic Normality Information Matrix Directional Derivative Asymptotic Variance 
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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Luc Pronzato
    • 1
  • Andrej Pázman
    • 2
  1. 1.French National Center for Scientific Research (CNRS)University of NiceSophia AntipolisFrance
  2. 2.Department of Applied Mathematics and StatisticsComenius UniversityBratislavaSlovakia

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