Efficient estimation of a linear transformation model for current status data via penalized splines

Minggen Lu, Yan Liu, Chin-Shang Li

Research output: Contribution to journalArticle


We propose a flexible and computationally efficient penalized estimation method for a semi-parametric linear transformation model with current status data. To facilitate model fitting, the unknown monotone function is approximated by monotone B-splines, and a computationally efficient hybrid algorithm involving the Fisher scoring algorithm and the isotonic regression is developed. A goodness-of-fit test and model diagnostics are also considered. The asymptotic properties of the penalized estimators are established, including the optimal rate of convergence for the function estimator and the semi-parametric efficiency for the regression parameter estimators. An extensive numerical experiment is conducted to evaluate the finite-sample properties of the penalized estimators, and the methodology is further illustrated with two real studies.

Original languageEnglish (US)
JournalStatistical Methods in Medical Research
StateAccepted/In press - Jan 1 2018
Externally publishedYes



  • Current status data
  • efficient estimation
  • goodness-of-fit
  • penalized spline
  • transformation model

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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