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

Zheng Li, Yunhao Wang and Wei Gao

Statistics & Probability Letters, 2025, vol. 226, issue C

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

Keywords: Central Kth moment subspace; Principal mixed-order moments; Elliptical distribution; Sufficient dimension reduction (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715225001518
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:226:y:2025:i:c:s0167715225001518

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.spl.2025.110506

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().