General partially linear additive transformation model with right-censored data

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.

Keywords:

• GPLATM
• maximum marginal likelihood estimation
• B spline polynomial
• right-censored data

AMS 2000 Subject Classifications::

• Primary: 62N02
• Secondary: 62G08

Acknowledgements

I sincerely thank the referees and associate editor for their valuable comments that lead to the great improved presentation of our work. This work is partially supported by National Natural Science Foundation of China (11201190, 11171112), Humanities and Social Fund of Ministry of Education in China (12YJC910004, 11YJC880071), A Project Funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions and Qinglan project in Jiangsu.