Empirical model-building and response surface
Empirical model-building and response surface
Maximum likelihood estimation of isotonic normal means with unknown variances
Journal of Multivariate Analysis
Determinant Maximization with Linear Matrix Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
Two classes of multiplicative algorithms for constructing optimizing distributions
Computational Statistics & Data Analysis
Improving updating rules in multiplicative algorithms for computing D-optimal designs
Computational Statistics & Data Analysis
A lower bound for discrimination information in terms of variation (Corresp.)
IEEE Transactions on Information Theory
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In this article, we discuss the optimal allocation problem in an experiment when a regression model is used for statistical analysis. Monotonic convergence for a general class of multiplicative algorithms for D-optimality has been discussed in the literature. Here, we provide an alternate proof of the monotonic convergence for D-criterion with a simple computational algorithm and furthermore show it converges to the D-optimality. We also discuss an algorithm as well as a conjecture of the monotonic convergence for A-criterion. Monte Carlo simulations are used to demonstrate the reliability, efficiency and usefulness of the proposed algorithms.