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Iterative Partial Least Squares with Right‐Censored Data Analysis: A Comparison to Other Dimension Reduction Techniques
Author(s) -
Huang Jie,
Harrington David
Publication year - 2005
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2005.040304.x
Subject(s) - covariate , partial least squares regression , statistics , mathematics , sufficient dimension reduction , dimension (graph theory) , generalized least squares , dimensionality reduction , accelerated failure time model , least squares function approximation , data set , standard error , regression analysis , regression , econometrics , computer science , artificial intelligence , estimator , pure mathematics
Summary In the linear model with right‐censored responses and many potential explanatory variables, regression parameter estimates may be unstable or, when the covariates outnumber the uncensored observations, not estimable. We propose an iterative algorithm for partial least squares, based on the Buckley–James estimating equation, to estimate the covariate effect and predict the response for a future subject with a given set of covariates. We use a leave‐two‐out cross‐validation method for empirically selecting the number of components in the partial least‐squares fit that approximately minimizes the error in estimating the covariate effect of a future observation. Simulation studies compare the methods discussed here with other dimension reduction techniques. Data from the AIDS Clinical Trials Group protocol 333 are used to motivate the methodology.