Connections between two classes of estimators for single‐index models

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Weichao Yang, Xu Guo, Niwen Zhou, Changliang Zou
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引用次数: 0

Abstract

Single‐index model is a very popular and powerful semiparametric model. As an improvement of the maximum rank correlation estimator, [[spiapacite]]bib1[[/spiapacite]] proposed the linearized maximum rank correlation estimator. We show that this estimator has some interesting connections with the distribution‐transformed least‐squares estimator for single‐index models. We also propose a rescaled distribution‐transformed least‐squares estimator, which is mathematically equivalent to the linearized maximum rank correlation estimator when the distribution of the response is absolutely continuous. Despite some nontrivial connections, the two estimation procedures are different in terms of motivations, interpretations, and applications. We discuss some of the differences between the two estimation procedures. This article is protected by copyright. All rights reserved.
单指标模型的两类估计量之间的联系
单指标模型是一种非常流行且功能强大的半参数模型。作为对最大秩相关估计器的改进,[[spiapacite]]bib1[[/spiapacite]]提出了线性化最大秩相关估计器。我们证明了这个估计量与单指标模型的分布变换最小二乘估计量有一些有趣的联系。我们还提出了一个重标化的分布变换最小二乘估计量,当响应分布绝对连续时,它在数学上等同于线性化的最大秩相关估计量。尽管有一些重要的联系,但这两种评估过程在动机、解释和应用方面是不同的。我们将讨论这两种估计过程之间的一些差异。这篇文章受版权保护。版权所有。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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