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

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Jaromír Antoch, Michal Černý, Ryozo Miura
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引用次数: 0

Abstract

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.

Abstract Image

线性模型中的 R 估计:算法、复杂性和挑战
本文的主要目的是讨论线性模型中稳健估计的某些计算问题,重点是 R 估计器。我们的重点是数值算法和计算效率,而不是统计特性。此外,我们还提出了 R 估计器的 "好 "方法应满足的一些算法属性,并展示了如何利用现有算法满足这些属性。我们举例说明了现有算法的优点和缺点。我们提出了两阶段方法,以尽量减少不良属性的影响。最后,我们对基于优化中内点法的新方法提出了挑战。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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