Efficient Computational Algorithm for Optimal Allocation in Regression Models

Abstract

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.

17 pages and 2 tables, accepted by Journal of Computational and Applied Mathematics in 2013 Journal of Computational and Applied Mathematics 2013

References in corpus (2)

• Monotonic convergence of a general algorithm for computing optimal designs
• Improvements on removing non-optimal support points in D-optimum design algorithms