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Robust integer-valued designs for linear random intercept models

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  • Rong-Xian Yue
  • Xiao-Dong Zhou

Abstract

This article considers the robust design problem for linear random intercept models with both departures from fixed effects and correlated errors on a finite design space. Two strategies are proposed. One is a worst-case method minimizing the maximum value of the MSE of estimates for the fixed effects over the departure. The other is an average-case method minimizing the average value of the MSE with respect to some priors for the class of departure functions and correlation structures of random errors. Two examples are given to show robust designs for two polynomial models.

Suggested Citation

  • Rong-Xian Yue & Xiao-Dong Zhou, 2018. "Robust integer-valued designs for linear random intercept models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(17), pages 4338-4354, September.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:17:p:4338-4354
    DOI: 10.1080/03610926.2017.1373820
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