Optimum covariate designs in a binary proper equi-replicate block design set-up
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The problem of finding the optimum covariate design (OCD) for the estimation of covariate parameters in a binary proper equi-replicate block (BPEB) design model with covariates, which cover a large class of designs in common use, is considered.About:
This article is published in Discrete Mathematics.The article was published on 2010-03-01 and is currently open access. It has received 8 citations till now. The article focuses on the topics: Covariate & Block design.read more
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Journal ArticleDOI
D-Optimal Designs for Covariate Models
TL;DR: In this article, an alternative upper bound to the determinant of the information matrix has been found through completely symmetric C-matrices for the regression coefficients; this upper bound includes the upper bound given in Dey and Mukerjee (2006) obtained through diagonal C-Matrices.
Journal ArticleDOI
Optimum Design for Estimation of Regression Parameters in Multi-Factor Set-Up
Ganesh Dutta,Premadhis Das +1 more
TL;DR: In this paper, the authors extended these results and proposed an extended mixed orthogonal array (EMOA) for the multi-factor set-up where the factorial effects involving at most t (≤m) factors are orthogonally estimable.
Journal ArticleDOI
Optimum designs for estimation of regression parameters in a balanced treatment incomplete block design set-up
Ganesh Dutta,Premadhis Das +1 more
TL;DR: In this paper, optimum covariate designs have been considered for the set-up of the balanced treatment incomplete block (BTIB) designs, which form an important class of test-control designs.
Journal ArticleDOI
A Review on Optimum Covariate Designs
TL;DR: In this paper, a short review of the present developments in this connection is presented, where the covariate effects were estimated with global optimality and a series of different design set-ups and proposed optimum covariate designs were considered.
Book ChapterDOI
Optimal Covariate Designs (OCDs): Scope of the Monograph
TL;DR: In this paper, the authors discuss the importance of optimal choice of covariates in linear models and provide a chapter-wise summary of the work covered and choice of various experimental design settings.
References
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Book
Linear statistical inference and its applications
TL;DR: Algebra of Vectors and Matrices, Probability Theory, Tools and Techniques, and Continuous Probability Models.
Journal ArticleDOI
Linear Statistical Inference and Its Applications.
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Optimal Design of Experiments
TL;DR: Experimental designs in linear models Optimal designs for Scalar Parameter Systems Information Matrices Loewner Optimality Real Optimality Criteria Matrix Means The General Equivalence Theorem Optimal Moment Matrices and Optimal Designs D-, A-, E-, T-Optimality Admissibility of moment and information matrices Bayes Designs and Discrimination Designs Efficient Designs for Finite Sample Sizes Invariant Design Problems Kiefer Optimality Rotatability and Response Surface Designs Comments and References Biographies Bibliography Index as discussed by the authors
Journal ArticleDOI
Linear Statistical Inference and its Applications
J. Aitchison,C. Radhakrishna Rao +1 more
TL;DR: Causal inference in statistics: An overview Linear Statistical Inference And Its Bayesian inference Wikipedia Springer Series in Statistics Stanford University Statistical Modeling, Causal Inference, and Social Science.