Title | Inverse regression estimation for censored data. |
Publication Type | Journal Article |
Year of Publication | 2011 |
Authors | Nadkarni, Nivedita V., Yingqi Zhao, and Michael R. Kosorok |
Journal | J Am Stat Assoc |
Volume | 106 |
Issue | 493 |
Pagination | 178-190 |
Date Published | 2011 Mar 01 |
ISSN | 0162-1459 |
Abstract | An inverse regression methodology for assessing predictor performance in the censored data setup is developed along with inference procedures and a computational algorithm. The technique developed here allows for conditioning on the unobserved failure time along with a weighting mechanism that accounts for the censoring. The implementation is nonparametric and computationally fast. This provides an efficient methodological tool that can be used especially in cases where the usual modeling assumptions are not applicable to the data under consideration. It can also be a good diagnostic tool that can be used in the model selection process. We have provided theoretical justification of consistency and asymptotic normality of the methodology. Simulation studies and two data analyses are provided to illustrate the practical utility of the procedure. |
DOI | 10.1198/jasa.2011.tm08250 |
Alternate Journal | J Am Stat Assoc |
Original Publication | Inverse regression estimation for censored data. |
PubMed ID | 21666842 |
PubMed Central ID | PMC3110674 |
Grant List | R29 CA075142 / CA / NCI NIH HHS / United States P01 CA142538-01 / CA / NCI NIH HHS / United States R01 CA075142-10 / CA / NCI NIH HHS / United States R01 CA075142 / CA / NCI NIH HHS / United States R01 CA075142-09A1 / CA / NCI NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States R01 CA075142-11 / CA / NCI NIH HHS / United States P30 ES010126 / ES / NIEHS NIH HHS / United States |
Inverse regression estimation for censored data.
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