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

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  • Li, Zheng
  • Wang, Yunhao
  • Gao, Wei

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.

Suggested Citation

  • Li, Zheng & Wang, Yunhao & Gao, Wei, 2025. "A principal mixed-order moments method for CKMS in dimension reduction," Statistics & Probability Letters, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:stapro:v:226:y:2025:i:c:s0167715225001518
    DOI: 10.1016/j.spl.2025.110506
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    References listed on IDEAS

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