Journal ArticleDOI
D-Optimal Designs for Covariate Models
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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.Abstract:
The problem of finding D-optimal designs in the presence of a number of covariates has been considered in the one-way set-up. This is an extension of Dey and Mukerjee (2006) in the sense that for fixed replication numbers of each treatment, 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. Because of the fact that a smaller class of C-matrices was used at the intermediate stage where the replication numbers were fixed, ultimately some optimal designs remained unidentified there. These designs have been identified here and thereby the conjecture made in Dey and Mukerjee (2006) has been settled.read more
Citations
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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
Design of near-optimal irregular fractional plans satisfying multi-optimality criteria
TL;DR: This paper attempts to find an optimal design which performs uniformly better with respect to multi-design-optimality criteria and demonstrates the method of comparison with an example along with its demerits in terms of design efficiency.
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Nearly optimal covariate designs—Part II
Ganesh Dutta,Bikas K. Sinha +1 more
TL;DR: In this article, the authors propose to discuss several examples from standard text books and re-visit these examples with a view to suggest optimal/nearly optimal designs for estimation of the covariate parameter(s).
Journal ArticleDOI
On the construction of optimal designs
TL;DR: In this article, two strategies for specifying additional data to be included with the data of a non-orthogonal design are presented, which increase the magnitude of the information matrix X and the orthogonality of the design matrix.
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.
Journal ArticleDOI
Linear Statistical Inference and Its Applications
TL;DR: Rao's Linear Statistical Inference and Its Applications as discussed by the authors is one of the earliest works in statistical inference in the literature and has been translated into six major languages of the world.
Journal ArticleDOI
$D$-Optimum Weighing Designs
TL;DR: In this article, the authors presented a list of the optimal designs for the problem of weighting k objects in chemical balance problems, and showed that for the most difficult case (n \equiv 3 (operatorname{mod} 4) = (n − 2k - 5) the optimum is known in all cases (n = (9, 11), (11, 15) and (12, 15).
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