Empirical likelihood is widely used in many statistical problems. In this article, we provide a
review of the empirical likelihood method, due to its significant development in recent years …

We propose and study properties of maximum likelihood estimators in the class of
conditional transformation models. Based on a suitable explicit parameterization of the …

The proliferation of electronic health records (EHRs) frames opportunities for using machine
learning to build models that help healthcare providers improve patient outcomes. However …

For regression analysis of interval-censored failure time data, Zhang et al.(2005)[40]
proposed an estimating equation approach to fit linear transformation models. In this paper …

The current penalized regression methods for selecting predictor variables and estimating
the associated regression coefficients in the sparse Cox model are mainly based on partial …

Time-to-event prediction has been an important practical task for longitudinal studies in
many fields such as manufacturing, medicine, and healthcare. While most of the …

Recent advances in the transformation model have made it possible to use this model for
analyzing a variety of censored survival data. For inference on the regression parameters …

Survival prediction is crucial to healthcare research, but is confined primarily to specific
types of data involving only the present measurements. This paper considers the more …

We consider the (profile) empirical likelihood inferences for the regression parameter (and
its any sub-component) in the semiparametric additive isotonic regression model where …

This paper focuses on comparing two means and finding a confidence interval for the
difference of two means with right-censored data using the empirical likelihood method …