多元响应回归充分降维的选择性评价

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY Journal of Statistical Planning and Inference Pub Date : 2023-09-01 DOI:10.1016/j.jspi.2023.02.003

Yuexiao Dong , Abdul-Nasah Soale , Michael D. Power

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引用次数: 2

摘要

本文综述了具有多元响应的充分降维估计。各种SDR方法的特点是逆回归SDR估计器或正回归SDR估计器。逆回归家族包括混合边际估计器、投影重抽样估计器和基于距离的估计器。另一方面，将普通最小二乘、偏最小二乘和半参数SDR估计量作为正回归族的估计量进行讨论。

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A selective review of sufficient dimension reduction for multivariate response regression

We review sufficient dimension reduction (SDR) estimators with multivariate response in this paper. A wide range of SDR methods are characterized as inverse regression SDR estimators or forward regression SDR estimators. The inverse regression family includes pooled marginal estimators, projective resampling estimators, and distance-based estimators. Ordinary least squares, partial least squares, and semiparametric SDR estimators, on the other hand, are discussed as estimators from the forward regression family.

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来源期刊

Journal of Statistical Planning and Inference 数学-统计学与概率论

CiteScore

2.10

自引率

11.10%

发文量

审稿时长

3-6 weeks

期刊介绍： The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists. We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.