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Sufficient dimension reduction based on distance‐weighted discrimination
Author(s) -
Randall Hayley,
Artemiou Andreas,
Qiao Xingye
Publication year - 2021
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12484
Subject(s) - mathematics , dimension (graph theory) , consistency (knowledge bases) , reduction (mathematics) , sufficient dimension reduction , dimensionality reduction , algorithm , pattern recognition (psychology) , artificial intelligence , statistics , computer science , combinatorics , discrete mathematics , geometry , regression
In this paper, we introduce a sufficient dimension reduction (SDR) algorithm based on distance‐weighted discrimination (DWD). Our methods is shown to be robust on the dimension p of the predictors in our problem, and it also utilizes some new computational results in the DWD literature to propose a computationally faster algorithm than previous classification‐based algorithms in the SDR literature. In addition to the theoretical results of similar methods we prove the consistency of our estimate for fixed p . Finally, we demonstrate the advantages of our algorithm using simulated and real datasets.