R-estimation in linear models: algorithms, complexity, challenges

被引：0
作者：
Antoch, Jaromir [1 ,2 ]
Cerny, Michal [2 ]
Miura, Ryozo [3 ]
机构：
[1] Charles Univ Prague, Fac Math & Phys, Dept Probabil Theory & Math Stat, Sokolovska 83, Prague 18675, Czech Republic
[2] Prague Univ Econ & Business, Fac Informat & Stat, Dept Econometr, Winston Churchill Sq 1938-4, Prague 13067, Czech Republic
[3] Tohoku Univ, Fac Econ, Tohoku Univ Campus,Aoba Ward, Sendai 9808576, Japan
来源：
COMPUTATIONAL STATISTICS | 2025年 / 40卷 / 01期
关键词：
Linear regression model; R-estimators; Least squares estimator; L-1-norm estimator; Iteratively reweighted least squares; Iterated weighted least squares; s-step R-estimators; Newton-like algorithm; Line and simplex search; Discrete and continuous optimization; Arrangement of hyperplanes; REWEIGHTED LEAST-SQUARES; RANK-BASED ESTIMATION; ROBUST REGRESSION; NUMERICAL-METHODS; COEFFICIENTS; PRINCIPAL;
D O I：
10.1007/s00180-024-01495-0
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
The main objective of this paper is to discuss selected computational aspects of robust estimation in the linear model with the emphasis on R-estimators. We focus on numerical algorithms and computational efficiency rather than on statistical properties. In addition, we formulate some algorithmic properties that a "good" method for R-estimators is expected to satisfy and show how to satisfy them using the currently available algorithms. We illustrate both good and bad properties of the existing algorithms. We propose two-stage methods to minimize the effect of the bad properties. Finally we justify a challenge for new approaches based on interior-point methods in optimization.
引用
收藏
页码：405 / 439
页数：35
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