Locally D-optimal designs for heteroscedastic polynomial measurement error models

Min-Jue Zhang and Rong-Xian Yue ()
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Min-Jue Zhang: Shanghai Normal University
Rong-Xian Yue: Shanghai Normal University

Metrika: International Journal for Theoretical and Applied Statistics, 2020, vol. 83, issue 6, No 1, 643-656

Abstract: Abstract This paper considers constructions of optimal designs for heteroscedastic polynomial measurement error models. Corresponding approximate design theory is developed by using corrected score function approach, which leads to non-concave optimisation problems. For the weighted polynomial measurement error model of degree p with some commonly used heteroscedastic structures, the upper bounds for the number of support points of locally D-optimal designs can be determined explicitly. A numerical example is given to show how heteroscedastic structures affect the optimal designs.

Keywords: Measurement error model; Heteroscedasticity; Corrected score function approach; Chebycheff system; Local D-optimality (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00184-019-00745-2

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