GUEST LECTURER
Professor Ming-Yen Cheng

AFFILIATION
Department of Mathematics, National Taiwan University

ABSTRACT
Data truncation is a problem in scientific investigations. So far, statistical models and inferences are mostly based on the assumption that the survival and truncation times are independent, which can be unrealistic in applications. In a nonparametric setting, we discuss identifiability of the conditional and unconditional survival and hazard functions when the survival times are subject to dependent truncation, namely, the survival time is dependent on the truncation time. A motivating example is discussed. Nonparametric kernel estimators of these unknowns are proposed. Usefulness of the nonparametric estimators are demonstrated through their theoretical
properties, an application to real data and a simulation study. This is a joint work with Peter Hall and You-Jun Yang.