The Annals of Statistics

Smoothed Cox regression

Dorota M. Dabrowska

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Abstract

Nonparametric regression was shown by Beran and McKeague and Utikal to provide a flexible method for analysis of censored failure times and more general counting processes models in the presence of covariates. We discuss application of kernel smoothing towards estimation in a generalized Cox regression model with baseline intensity dependent on a covariate. Under regularity conditions we show that estimates of the regression parameters are asymptotically normal at rate root-n, and we also discuss estimation of the baseline cumulative hazard function and related parameters.

Article information

Source
Ann. Statist., Volume 25, Number 4 (1997), 1510-1540.

Dates
First available in Project Euclid: 9 September 2002

Permanent link to this document
https://projecteuclid.org/euclid.aos/1031594730

Digital Object Identifier
doi:10.1214/aos/1031594730

Mathematical Reviews number (MathSciNet)
MR1463563

Zentralblatt MATH identifier
0936.62046

Subjects
Primary: 62G05: Estimation 62M09: Non-Markovian processes: estimation

Keywords
Kernel estimation counting processes hazard functions estimation

Citation

Dabrowska, Dorota M. Smoothed Cox regression. Ann. Statist. 25 (1997), no. 4, 1510--1540. doi:10.1214/aos/1031594730. https://projecteuclid.org/euclid.aos/1031594730


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