General partially linear additive transformation model with right-censored data

Lin Liu, Jianbo Li and Riquan Zhang

Journal of Applied Statistics, 2014, vol. 41, issue 10, 2257-2269

Abstract: We propose a class of general partially linear additive transformation models (GPLATM) with right-censored survival data in this work. The class of models are flexible enough to cover many commonly used parametric and nonparametric survival analysis models as its special cases. Based on the B spline interpolation technique, we estimate the unknown regression parameters and functions by the maximum marginal likelihood estimation method. One important feature of the estimation procedure is that it does not need the baseline and censoring cumulative density distributions. Some numerical studies illustrate that this procedure can work very well for the moderate sample size.

Date: 2014
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DOI: 10.1080/02664763.2014.909788

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