CVX-based algorithms for constructing various optimal regression designs

Weng Kee Wong, Julie Zhou

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

CVX-based numerical algorithms are widely and freely available for solving convex optimization problems but their applications to solve optimal design problems are limited. Using the CVX programs in MATLAB, we demonstrate their utility and flexibility over traditional algorithms in statistics for finding different types of optimal approximate designs under a convex criterion for nonlinear models. They are generally fast and easy to implement for any model and any convex optimality criterion. We derive theoretical properties of the algorithms and use them to generate new A-, c-, D- and E-optimal designs for various nonlinear models, including multi-stage and multi-objective optimal designs. We report properties of the optimal designs and provide sample CVX program codes for some of our examples that users can amend to find tailored optimal designs for their problems.

Original languageEnglish
Pages (from-to)374-391
Number of pages18
JournalCanadian Journal of Statistics
Volume47
Issue number3
DOIs
Publication statusPublished - 2019 Sept

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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