Stephen Portnoy, University of Illinois at Urbana-Champaign

Some surprising facts in survival quantile estimation
Date
Nov 23, 2010, 12:30 pm1:30 pm
Location
213 - Sherrerd Hall
Event Description

While censored data is sufficiently common to have generated an enormous field of applied statistical research, the basic model for such data is also sufficiently non- standard to provide ample surprises to the statistical theorist, especially one who is too quick to assume regularity conditions. Here we show that estimators of the sur- vival quantile function based on assuming additional information about the censoring distribution behave more poorly than estimators (like the inverse of Kaplan-Meier) that discard this information. This phenomenon will be explored with special em- phasis on the Powell estimator, which assumes that all censoring times are observed.