Optimal designs for regression models using the second-order least squares estimator

Date

2017-07-31

Authors

Yin, Yue
Zhou, Julie

Journal Title

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Volume Title

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Abstract

We investigate properties and numerical algorithms for A- and D-optimal regression designs based on the second-order least squares estimator (SLSE). Several results are derived, including a characterization of the A-optimality criterion. We can formulate the optimal design problems under SLSE as semide nite programming or convex optimization problems and we show that the resulting algorithms can be faster than more conventional multiplicative algorithms, especially in nonlinear models. Our results also indicate that the optimal designs based on the SLSE are more e cient than those based on the ordinary least squares estimator, provided the error distribution is highly skewed.

Description

The final publication is expected to appear in Volume 27, Number 4, October 2017

Keywords

A-optimal design, convex optimization, D-optimal design, multiplicative algorithm, nonlinear model, SeDuMi, transformation invariance

Citation