Optimal experimental designs for ordinal models with mixed factors for industrial and healthcare applications

Joshua Lukemire, Abhyuday Mandal, Weng Kee Wong

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Models with ordinal outcomes are an important part of generalized linear models and design issues for them are less studied, especially when the model has discrete and continuous factors. We propose an effective and flexible Particle Swarm Optimization (PSO) algorithm for finding locally D-optimal approximate designs for experiments with ordinal outcomes modeled using the cumulative logit link. We apply our technique to obtain a locally D-optimal approximate design for an odor removal experiment with both discrete and continuous factors and show that this design is superior to the design obtained by discretizing the continuous factor. Additionally, we find a pseudo-Bayesian D-optimal approximate design for this problem and study the performance of both designs under a range of plausible parameter values. We also (i) demonstrate PSO’s versatility by finding locally D-optimal approximate designs for a manufacturing example with surface defects and multiple continuous factors, and (ii) use PSO to find other optimal designs for estimating percentiles in a dose-response study.

Original languageEnglish
Pages (from-to)184-196
Number of pages13
JournalJournal of Quality Technology
Volume54
Issue number2
DOIs
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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