D-QPSO: A Quantum-Behaved Particle Swarm Technique for Finding D-Optimal Designs With Discrete and Continuous Factors and a Binary Response. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Lukemire, Joshua; Mandal, Abhyuday; Wong, Weng Kee
• Is Part Of: Technometrics. Volume 61:Issue 1(2019)
• Page Start: 77
• Page End: 87
• Publication Date: 2019-01-02
• Subjects: Approximate design -- Design efficiency -- Equivalence theorem -- Exact design -- Pseudo-Bayesian design; Statistical physics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; Engineering -- Statistical methods -- Periodicals; 519.5
• DOI: 10.1080/00401706.2018.1439405
• Languages: English
• ISSNs: 0040-1706

Abstract

ABSTRACT: Identifying optimal designs for generalized linear models with a binary response can be a challenging task, especially when there are both discrete and continuous independent factors in the model. Theoretical results rarely exist for such models, and for the handful that do, they usually come with restrictive assumptions. In this article, we propose the d -QPSO algorithm, a modified version of quantum-behaved particle swarm optimization, to find a variety of D -optimal approximate and exact designs for experiments with discrete and continuous factors and a binary response. We show that the d -QPSO algorithm can efficiently find locally D -optimal designs even for experiments with a large number of factors and robust pseudo-Bayesian designs when nominal values for the model parameters are not available. Additionally, we investigate robustness properties of the d -QPSO algorithm-generated designs to various model assumptions and provide real applications to design a bio-plastics odor removal experiment, an electronic static experiment, and a 10-factor car refueling experiment. Supplementary materials for the article are available online.