A heuristic approach to combat multicollinearity in least trimmed squares regression analysis

被引：17

作者：

Roozbeh, Mandi ^{[1
]}

Babaie-Kafaki, Saman ^{[1
]}

Sadigh, Alireza Naeimi ^{[1
]}

机构：

[1] Semnan Univ, Fac Math Stat & Comp Sci, POB 35195-363, Semnan, Iran

来源：

APPLIED MATHEMATICAL MODELLING | 2018年 / 57卷

基金：

美国国家科学基金会;

关键词：

Breakdown point; Constrained optimization; Heuristic algorithm; Least trimmed squares estimator; Multicollinearity; Ridge estimation; PRINCIPAL COMPONENT REGRESSION; RIDGE; ESTIMATOR;

D O I：

10.1016/j.apm.2017.11.011

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In order to down-weight or ignore unusual data and multicollinearity effects, some alternative robust estimators are introduced. Firstly, a ridge least trimmed squares approach is discussed. Then, based on a penalization scheme, a nonlinear integer programming problem is suggested. Because of complexity and difficulty, the proposed optimization problem is solved by a tabu search heuristic algorithm. Also, the robust generalized cross validation criterion is employed for selecting the optimal ridge parameter. Finally, a simulation case and two real-world data sets are computationally studied to support our theoretical discussions. (C) 2017 Elsevier Inc. All rights reserved.

引用

页码：105 / 120

页数：16

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