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Sparse partial least‐squares regression for high‐throughput survival data analysis
Author(s) -
Lee Donghwan,
Lee Youngjo,
Pawitan Yudi,
Lee Woojoo
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5975
Subject(s) - partial least squares regression , feature selection , computer science , regression , proportional hazards model , latent variable , selection (genetic algorithm) , regression analysis , dimension (graph theory) , elastic net regularization , variable (mathematics) , statistics , data mining , algorithm , artificial intelligence , mathematics , machine learning , mathematical analysis , pure mathematics
The partial least‐square (PLS) method has been adapted to the Cox's proportional hazards model for analyzing high‐dimensional survival data. But because the latent components constructed in PLS employ all predictors regardless of their relevance, it is often difficult to interpret the results. In this paper, we propose a new formulation of sparse PLS (SPLS) procedure for survival data to allow simultaneous sparse variable selection and dimension reduction. We develop a computing algorithm for SPLS by modifying an iteratively reweighted PLS algorithm and illustrate the method with the Swedish and the Netherlands Cancer Institute breast cancer datasets. Through the numerical studies, we find that our SPLS method generally performs better than the standard PLS and sparse Cox regression methods in variable selection and prediction. Copyright © 2013 John Wiley & Sons, Ltd.