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.
CITATION STYLE
Dabrowska, D. M. (1997). Smoothed cox regression. Annals of Statistics, 25(4), 1510–1540. https://doi.org/10.1214/aos/1031594730
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