2026-10-06T07:10:32Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34070953 2026-10-05T16:39:04Z category_45 category_734 category_931 category_811 portal_87 item_type_3 month_year_10_2026

High-Dimensional Sliced Inverse Kendall’s Tau Estimation for Heavy-Tailed Data Jiaqi Li (220961) Shuaida He (22988591) Xin Chen (14149) Sociology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Elliptical distribution Multivariate spatial Kendall’s tau Sliced average variance estimation Sliced inverse regression Sufficient dimension reduction <p>Sufficient dimension reduction is an important branch of statistical learning, which performs an optimal projection of covariates under a supervised learning framework. Most of the existing sufficient dimension reduction algorithms require strict restrictions on the distribution of models, making them sensitive to heavy-tailed predictors and outliers. In this article, we propose a novel method called Sliced Inverse Kendall’s tau Estimation (SIKE) designed for analyzing heavy-tailed, elliptically distributed and high-dimensional data that find wide application, especially in finance and economics. Compared with other existing methods, SIKE does not require computing median or conditional median, thus imposes milder conditions on the method and increases the precision of estimation. Furthermore, a remedial algorithm is introduced in discouraging situation, which enhances the general adaptability of our method. We investigate the theoretical properties of SIKE as the dimension p diverges with sample size n. Extensive simulation studies show that SIKE performs significantly well for various scenarios. Analyses of asset pricing data and housing data also demonstrate the effectiveness of our proposed method.</p> 2026-10-05T16:39:04Z Dataset Dataset 10.6084/m9.figshare.34070953.v1 https://figshare.com/articles/dataset/High-Dimensional_Sliced_Inverse_Kendall_s_Tau_Estimation_for_Heavy-Tailed_Data/34070953 CC BY 4.0