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Stochastic Distance Optimality

  • Erkki P. Liski
  • Nripes K. Mandal
  • Kirti R. Shah
  • Bikas K. Sinha
Part of the Lecture Notes in Statistics book series (LNS, volume 163)

Abstract

Model(s): Discrete design models and regression models Experimental domains: Binary design set-up and T = [0, 1] and [−1,1] for continuous regressors Optimality criteria: Maximization of distance optimality functional Major tools: Probability inequalities, Schur convexity, invariance Optimality results: Optimal regression designs, optimal designs under CRD and BIBD set-up Thrust: Non-standard optimality functional, normality of error distribution This Chapter addresses optimality issues for a non-standard optimality criterion viz., the distance optimality criterion-originally introduced in Sinha (1970). Both discrete and regression design models are studied and specific optimality results are presented. This criterion has gained momentum only recently.

Keywords

Information Matrix Coverage Probability Distance Optimality Less Square Estimator Hadamard Matrix 
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 Science+Business Media New York 2002

Authors and Affiliations

  • Erkki P. Liski
    • 2
  • Nripes K. Mandal
    • 1
  • Kirti R. Shah
    • 4
  • Bikas K. Sinha
    • 3
  1. 1.Department of StatisticsCalcutta UniversityCalcuttaIndia
  2. 2.Department of Mathematics, Statistics, and PhilosophyUniversity of TampereTampereFinland
  3. 3.Stat-Math DivisonIndian Statistical InstituteCalcuttaIndia
  4. 4.Department of Statistics and Actuarial ScienceUniversity of WaterlooWaterlooCanada

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