Abstract #301538

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JSM 2002 Abstract #301538
Activity Number: 281
Type: Contributed
Date/Time: Wednesday, August 14, 2002 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics*
Abstract - #301538
Title: Adaptive Goodness-of-Fit with Incomplete Data
Author(s): Edsel Pena*+
Affiliation(s): University of South Carolina
Address: LeConte College, Columbia, South Carolina, 29208, USA
Keywords: Generalized Neyman tests ; smooth goodness-of-fit tests ; Schwarz information criterion
Abstract:

The use of hazard functions is a natural and convenient way of specifying failure-time models in reliability and biostatistical settings, as well as in other fields. It is typical in such studies, where the primary outcome variable is time-to-event occurrence, to have right-censored data. In Pena (Ann. Stat., 1998), a general hazard-based class of goodness-of-fit tests was proposed, but an existing deficiency of this class is the need to pre-specify a smoothing order. It was demonstrated that the power of the tests depended to a great extent on this smoothing order. In this talk, I will present a method for determining the smoothing order in an adaptive or data-dependent manner. The advantages of this adaptive scheme will be demonstrated theoretically and through Monte Carlo studies.

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