Optimal Covariate Designs: Theory and Applications [Paperback]

This book primarily addresses the optimality aspects of covariate designs. A covariate model is a combination of ANOVA and regression models. Optimal estimation of the parameters of the model using a suitable choice of designs is of great importance; as such choices allow experimenters to extract maximum information for the unknown model parameters. The main emphasis of this monograph is to start with an assumed covariate model in combination with some standard ANOVA set-ups such as CRD, RBD, BIBD, GDD, BTIBD, BPEBD, cross-over, multi-factor, split-plot and strip-plot designs, treatment control designs, etc. and discuss the nature and availability of optimal covariate designs. In some situations, optimal estimations of both ANOVA and the regression parameters are provided. Global optimality and D-optimality criteria are mainly used in selecting the design. The standard optimality results of both discrete and continuous set-ups have been adapted, and several novel combinatorial techniques have been applied for the construction of optimum designs using Hadamard matrices, the Kronecker product, Rao-Khatri product, mixed orthogonal arrays to name a few.

Chapter 1. Optimal Covariate Designs: Scope of the Monograph.- Chapter 2. OCDs in Completely Randomized Design Set-Up.- Chapter 3. OCDs in Randomized Block Design Set-Up.- Chapter 4. OCDs in Balanced Incomplete Block Design Set-Up.- Chapter 5. OCDs in Group Divisible Design Set-Up.- Chapter 6. OCDs in Binary Proper Equireplicate Block Design Set-Up.- Chapter 7. OCDs in Balanced Treatment Incomplete Block Design Set-Up.- Chapter 8. Miscellaneous other Topics and Issues.- Chapter 9. Applications of the Theory of OCDs.

Premadhis Das is a Senior Professor in the Department of Statistics, University of Kalyani, Inlƒ7