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Advance of the sufficient dimension reduction
Author(s) -
Hang Weiqiang,
Xia Yingcun
Publication year - 2020
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1516
Subject(s) - sliced inverse regression , dimensionality reduction , dimension (graph theory) , sufficient dimension reduction , reduction (mathematics) , semiparametric regression , multivariate statistics , statistics , regression , regression analysis , computer science , statistical model , econometrics , mathematics , artificial intelligence , geometry , pure mathematics
The sufficient dimension reduction of Li has been seen a steady development in the past 30 years in both methodology and application. The main approaches can be categorized into two groups: The inverse regression methods and forward regression methods. In this survey, we briefly discuss advances of methods and present problems that needs further investigation in the second group. This article is categorized under: Statistical Models > Multivariate Models Statistical and Graphical Methods of Data Analysis > Dimension Reduction Statistical Models > Semiparametric Models