A new logarithmic multiplicative distortion for correlation analysis

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Statistical Analysis and Data Mining Pub Date : 2024-08-23 DOI:10.1002/sam.11708

Siming Deng, Jun Zhang

引用次数: 0

Abstract

We study the Pearson correlation coefficient in a logarithmic manner under the presence of multiplicative distortion measurement errors. In this context, the observed variables with logarithmic transformation are distorted in multiplicative fashions by an observed confounding variable. The proposed multiplicative distortion model in this paper is applied to analyze positive variables. We utilize the conditional mean calibration and the conditional absolute mean calibration methods to obtain the calibrated variables. Furthermore, we propose confidence intervals based on asymptotic normality, empirical likelihood, and jackknife empirical likelihood. Simulation studies demonstrate the effectiveness of the proposed estimation procedure, and a real‐world example is analyzed to illustrate its practical application.

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用于相关分析的新对数乘法失真

我们研究了存在乘法扭曲测量误差情况下的对数皮尔逊相关系数。在这种情况下，具有对数变换的观测变量会被观测到的混杂变量以乘法方式扭曲。本文提出的乘法失真模型适用于分析正变量。我们利用条件均值校准法和条件绝对均值校准法获得校准变量。此外，我们还提出了基于渐近正态性、经验似然法和千斤顶经验似然法的置信区间。模拟研究证明了所建议的估计程序的有效性，并分析了一个实际案例来说明其实际应用。

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

Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.20

自引率

7.70%

发文量

期刊介绍： Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.