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Optimal Regression Designs in Asymmetric Domains

  • 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): Fixed coefficients regression (FCR), random coefficients regression (RCR) models (single factor linear, quadratic and cubic) Experimental domains: [0, 1], [0, h] and (h, H] Optimality criteria: Minimization of optimality functionals Major tools: de la Garza (DLG) phenomenon and Loewner order domination (LOD) of information matrices for search reduction Optimality results: Specific optimal designs under regression models for estimation, prediction and inverse prediction — all under continuous design theory Thrust: Asymmetric experimental domains

Keywords

Optimal Design Quadratic Regression Experimental Domain Integrate Mean Square Error Information Matrice 
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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