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Optimal Designs for Covariates’ Models with Structured Intercept Parameter

  • 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 with controllable covariates or multifactor linear regression models with structured intercept parameter Experimental domains: Binary set-up for discrete designs and T = [−1,1] for covariates Optimality criteria: Most efficient estimation of treatment parameters and covariate parameters Major tools: Mutually orthogonal latin squares and Hadamard matrices Optimality results: Optimal designs under CRD, RBD and BIBD set-up Thrust: Combinatorial optimization in terms of orthogonality (i) between factors versus blocks and treatments and also (ii) within the factors with levels ±1

Keywords

Orthogonal Array Information Matrix Randomize Block Design Incidence Matrix Cyclical Permutation 
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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