有测量误差的部分函数线性量回归。

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Mengli Zhang, Lan Xue, Carmen D Tekwe, Yang Bai, Annie Qu
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引用次数: 0

摘要

在传统回归分析中忽略测量误差会导致估计和推断结果出现偏差。当容易产生误差的协变量是函数曲线时,减少这种偏差具有挑战性。在本文中,我们为具有函数值测量误差的部分函数线性量化模型提出了一种新的修正损失函数。我们建立了函数系数估计器和参数系数估计器的渐近特性。我们还通过模拟研究证明了所提方法的有限样本性能,并将其应用于一项儿童肥胖症研究的数据中,从而说明了该方法的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PARTIALLY FUNCTIONAL LINEAR QUANTILE REGRESSION WITH MEASUREMENT ERRORS.

Ignoring measurement errors in conventional regression analyses can lead to biased estimation and inference results. Reducing such bias is challenging when the error-prone covariate is a functional curve. In this paper, we propose a new corrected loss function for a partially functional linear quantile model with function-valued measurement errors. We establish the asymptotic properties of both the functional coefficient and the parametric coefficient estimators. We also demonstrate the finite-sample performance of the proposed method using simulation studies, and illustrate its advantages by applying it to data from a children obesity study.

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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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