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D. Zeng and D. Y. Lin, “Efficient Estimation of Semiparametric Transformation Models For Counting Processes,” Biometrika, Vol. 93, No. 3, 2006, pp. 627-640. doi:10.1093/biomet/93.3.627

has been cited by the following article:

• TITLE: Variable Selection for Partially Linear Varying Coefficient Transformation Models with Censored Data

  AUTHORS: Jiang Du, Zhongzhan Zhang, Ying Lu

  KEYWORDS: Variable Selection; Maximum Likelihood Estimation; Spline Smoothing

  JOURNAL NAME: Open Journal of Statistics, Vol.2 No.5, December 27, 2012

  ABSTRACT: In this paper, we study the problem of variable selection for varying coefficient transformation models with censored data. We fit the varying coefficient transformation models by maximizing the marginal likelihood subject to a shrink- age-type penalty, which encourages sparse solutions and hence facilitates the process of variable selection. We further provide an efficient computation algorithm to implement the proposed methods. A simulation study is conducted to evaluate the performance of the proposed methods and a real dataset is analyzed as an illustration.