A principal mixed-order moments method for CKMS in dimension reduction

IF 0.7 4区数学 Q3 STATISTICS & PROBABILITY

Statistics & Probability Letters Pub Date : 2025-08-05 DOI:10.1016/j.spl.2025.110506

Zheng Li , Yunhao Wang , Wei Gao

引用次数: 0

Abstract

This paper presents a new Sufficient Dimension Reduction (SDR) approach, termed the Principal Mixed-order Moments (PMoM) method, for estimating the central

K

th moment subspace (CKMS). We develop a computational algorithm to implement PMoM and establish its consistency properties. To evaluate its effectiveness and efficiency, we conduct Monte Carlo simulations. Notably, PMoM demonstrates strong performance, especially in cases where the variance of individual components varies significantly, particularly under an elliptical distribution of predictor variables. Real data analysis in the Energy Efficiency dataset confirms the effectiveness of PMoM, highlighting its practicality in high-dimensional data reduction.

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CKMS降维的一种主要混合阶矩法

本文提出了一种新的充分降维方法——主混合阶矩法，用于估计中心第k阶矩子空间（CKMS）。我们开发了一种计算算法来实现PMoM，并建立了它的一致性。为了评估其有效性和效率，我们进行了蒙特卡洛模拟。值得注意的是，PMoM表现出很强的性能，特别是在个别成分的方差变化很大的情况下，特别是在预测变量的椭圆分布下。能源效率数据集的实际数据分析证实了PMoM的有效性，突出了其在高维数据约简中的实用性。

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

Statistics & Probability Letters 数学-统计学与概率论

CiteScore

1.60

自引率

0.00%

发文量

173

审稿时长

6 months

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