A note on a transformation under censoring with application to partial least squares regression

Sanjib Basu and Nader Ebrahimi

Statistics & Probability Letters, 2008, vol. 78, issue 10, 1161-1164

Abstract: In many applications of statistics, one is interested in the characteristics of a time-to-event variable Y, which, by itself, is not observable. Instead, one observes T=min(Y,Z) where Z is a censoring variable. Such data are common in biomedical, engineering and other applications as well as in a competing risks set-up. In this note, we provide a transformation based on the survival function of the censoring variable which allows one to recover the conditional expectation of Y from the observable T. We discuss various ramifications of this result and describe its application in partial least squares regression of censored time-to-event data.

Date: 2008
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