Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81058
Title: A class of semiparametric transformation cure models for interval-censored failure time data
Authors: Li, S
Hu, T
Zhao, X 
Sun, J
Keywords: EM algorithm
Interval censoring
Maximum likelihood estimation
Transformation cure models
Issue Date: 2019
Publisher: North-Holland
Source: Computational statistics and data analysis, 2019, v. 133, p.153-165 How to cite?
Journal: Computational statistics and data analysis 
Abstract: This paper discusses regression analysis of interval-censored failure time data with a cured subgroup under a general class of semiparametric transformation cure models. For inference, a novel and stable expectation maximization (EM) algorithm with the use of Poisson variables is developed to overcome the difficulty in maximizing the observed data likelihood function with complex form. The asymptotic properties of the resulting estimators are established and in particular, the estimators of regression parameters are shown to be semiparametrically efficient. The numerical results obtained from a simulation study indicate that the proposed approach works well for practical situations. An application to a set of data on children's mortality is also provided.
URI: http://hdl.handle.net/10397/81058
EISSN: 0167-9473
DOI: 10.1016/j.csda.2018.09.008
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