高维单分量偏最小二乘回归估计量的渐近分布

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-12-23 DOI:10.1002/cjs.11755

Jerónimo Basa, R. Dennis Cook, Liliana Forzani, Miguel Marcos

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引用次数: 0

摘要

：在线性回归模型的单分量偏最小二乘法中，随着样本量和预测因子数量的增加，我们确定了用户选择的系数估计器的单变量线性组合的渐近正态分布，并给出了相应的渐近置信度和预测区间。模拟研究和多巴胺数据集的分析用于支持我们的理论渐近结果及其实际应用。《加拿大统计杂志》xx:1-25；20？？©20？？加拿大统计学会

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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Asymptotic distribution of one-component partial least squares regression estimators in high dimensions

In a one-component partial least squares fit of a linear regression model, we find the asymptotic normal distribution, as the sample size and number of predictors approach infinity, of a user-selected univariate linear combination of the coefficient estimator and give corresponding asymptotic confidence and prediction intervals. Simulation studies and an analysis of a dopamine dataset are used to support our theoretical asymptotic results and their practical application.

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来源期刊

Canadian Journal of Statistics-Revue Canadienne De Statistique 数学-统计学与概率论

CiteScore

1.40

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Canadian Journal of Statistics is the official journal of the Statistical Society of Canada. It has a reputation internationally as an excellent journal. The editorial board is comprised of statistical scientists with applied, computational, methodological, theoretical and probabilistic interests. Their role is to ensure that the journal continues to provide an international forum for the discipline of Statistics. The journal seeks papers making broad points of interest to many readers, whereas papers making important points of more specific interest are better placed in more specialized journals. The levels of innovation and impact are key in the evaluation of submitted manuscripts.