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

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  • Basu, Sanjib
  • Ebrahimi, Nader

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.

Suggested Citation

  • Basu, Sanjib & Ebrahimi, Nader, 2008. "A note on a transformation under censoring with application to partial least squares regression," Statistics & Probability Letters, Elsevier, vol. 78(10), pages 1161-1164, August.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:10:p:1161-1164
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    References listed on IDEAS

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    1. Sanjib Basu & Ananda Sen & Mousumi Banerjee, 2003. "Bayesian analysis of competing risks with partially masked cause of failure," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 77-93, January.
    2. Lim, Johan & Lee, Sungim & Choi, Hyungwon, 2006. "Information loss from censoring in rank-based procedures," Statistics & Probability Letters, Elsevier, vol. 76(16), pages 1705-1713, October.
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