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Smooth estimation of mean residual life under random censoring

Yogendra P. Chaubey and Arusharka Sen

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Abstract

We propose here a smooth estimator of the mean residual life function based on randomly censored data. This is derived by smoothing the product-limit estimator using the Chaubey-Sen technique (Chaubey and Sen (1998)). The resulting estimator does not suffer from boundary bias as is the case with standard kernel smoothing. The asymptotic properties of the estimator are investigated. We establish strong uniform consistency and asymptotic normality. This complements the work of Chaubey and Sen (1999) which considered a similar estimation procedure in the case of complete data. It is seen that the properties are similar, though technically more difficult to prove, to those in the complete data case with appropriate modifications due to censoring.

Chapter information

Source
N. Balakrishnan, Edsel A. Peña and Mervyn J. Silvapulle, eds., Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008), 35-49

Dates
First available in Project Euclid: 1 April 2008

Permanent link to this document
https://projecteuclid.org/euclid.imsc/1207058262

Digital Object Identifier
doi:10.1214/193940307000000031

Subjects
Primary: 62G05: Estimation 62G20: Asymptotic properties
Secondary: 62G07: Density estimation

Keywords
asymptotics Hille’s theorem mean residual life random censoring smoothing survival function

Rights
Copyright © 2008, Institute of Mathematical Statistics

Citation

Chaubey, Yogendra P.; Sen, Arusharka. Smooth estimation of mean residual life under random censoring. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen, 35--49, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2008. doi:10.1214/193940307000000031. https://projecteuclid.org/euclid.imsc/1207058262


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References

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