|
Activity Number:
|
289
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Nonparametric Statistics
|
| Abstract - #309202 |
|
Title:
|
Nonparametric Estimation of Mean Residual Life Function
|
|
Author(s):
|
Shufang Liu*+ and Sujit Ghosh
|
|
Companies:
|
North Carolina State University and North Carolina State University
|
|
Address:
|
3520 Cum laude Ct, Raleigh, NC, 27606,
|
|
Keywords:
|
Mean residual life function ; Right-censored data ; Scale mixtures ; Smooth nonparametric estimate
|
|
Abstract:
|
The mean residual life function (mrlf) of a subject is defined as the average residual lifetime of the subject given that the subject has survived up to a given time point. It is well known that under mild regularity conditions an mrlf completely determines the probability distribution of the subjects' lifetime. In practice, the advantage of the mrlf over the more popularly used hazard function lies in its interpretation in many applications where the primary goal is often to characterize the remaining life expectancy of a subject instead of the instantaneous failure rate. A smooth nonparametric estimator of the mrlf is proposed using scale mixtures of the empirical estimate of the mrlf based on right-censored data. Asymptotic properties are established. Empirical performances of the proposed estimator are studied based on simulated data sets and a real dataset.
|
