Censored Regression Quantiles

نویسندگان

  • Stephen Portnoy
  • Stephen PORTNOY
چکیده

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of the American Statistical Association. Using quantile regression to analyze survival times offers an valuable complement to traditional Cox proportional hazards modelling. Unfortunately , this approach has been hampered by the lack of a conditional quantile estimator for censored data that is directly analogous to the Kaplan-Meier estimator and applies under standard assumptions for censored regression models. Here a recursively reweighted esti-mator of the regression quantile process is developed as a direct generalization of the Kaplan-Meier estimator. Specifically, the asymptotic behavior is directly analogous to that of the Kaplan-Meier estimator, and computation is essentially equivalent to current simplex methods for the quantile process in the uncensored case. Some preliminary examples suggest the strong potential of these methods as a complement to the use of Cox models.

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تاریخ انتشار 2008