Criterion-robust optimal designs for model discrimination and parameter estimation: Multivariate polynomial regression case

Min Hsiao Tsai, Mei-Mei Zen

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Consider the problem of discriminating between two polynomial regression models on the q-cube [-1, 1] q, q ≥ 2, and estimating parameters in the models. To find designs which are efficient for both model discrimination and parameter estimation, Zen and Tsai (2002) proposed a multiple-objective optimality criterion for the univariate case. In this work, taking the same M γ-criterion which uses weight γ (0 ≤ γ ≤ 1) for model discrimination and 1 - γ for parameter estimation, the corresponding M γ-optimal product design is investigated. Based on the maximin principle on the M γ-efficiency of any M γ′-optimal product design, a criterion-robust optimal product design is proposed.

Original languageEnglish
Pages (from-to)591-601
Number of pages11
JournalStatistica Sinica
Volume14
Issue number2
Publication statusPublished - 2004 Apr

All Science Journal Classification (ASJC) codes

  • Mathematics(all)
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Criterion-robust optimal designs for model discrimination and parameter estimation: Multivariate polynomial regression case'. Together they form a unique fingerprint.

  • Cite this