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. Nonparametric kernel estimators of these unknowns are proposed. Usefulness of the nonparametric estimators is demonstrated through their theoretical properties, an application and a simulation study.

AMS Subject Classification (1991): 62N02, 62F12

Keyword(s): Conditional distribution, dependent truncation, hazard rate, identifiability, kernel, nonparametric estimation, survival function, truncation

Received January 15, 2007. (Registered under 5972/2009.)