Optimal designs for regression models using the second-order least squares estimator
Date
2017-07-31
Authors
Yin, Yue
Zhou, Julie
Journal Title
Journal ISSN
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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