Construction of D-Optimal Experimental Designs for Nonparametric Regression Models

被引：0
|
作者：
Denisov V.I. [1 ]
Timofeev V.S. [1 ]
Kamenev P.A. [1 ]
机构：
[1] Novosibirsk State Technical University, pr. Karla Marksa 20, Novosibirsk
来源：
Journal of Applied and Industrial Mathematics | 2018年 / 12卷 / 2期
基金：
俄罗斯科学基金会;
关键词：
D-optimal design; experimental design; Fisher information matrix; kernel smoothing; nonparametric regression;
D O I：
10.1134/S1990478918020047
中图分类号：
学科分类号：
摘要：
Under study is the problem of a D-optimal experimental design for the problem of nonparametric kernel smoothing. Modification is proposed for the process of calculating the Fisher information matrix. D-optimal designs are constructed for one and several target points for the problems of nonparametric kernel smoothing using a uniform kernel, the Gauss and Epanechnikov kernels. Comparison is performed between Fedorov’s algorithm and direct optimization methods (such as the Nelder–Mead method and the method of differential evolution). The features of the application of the optimality criterion for the experimental design of the problems with several target points were specified for the cases of various kernels and bandwidths. © 2018, Pleiades Publishing, Ltd.
引用
收藏
页码：234 / 242
页数：8
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