Censored data, where the exact value of an observation is not fully observed, poses a challenge in statistical modelling. Traditional regression approaches often fail to adequately handle such leading biased estimates and inaccurate predictions. In this study, we propose novel anti-regression framework specifically designed for censored data The integrates advanced techniques incorporates mecha...

In this paper, we develop a Mean Empirical Likelihood (MeanEL) method for right censored data. This MeanEL approach is based on traditional empirical likelihood methods but uses synthetic data to construct an EL ratio statistics, which shown have χ2 limiting distribution. Different simulation studies show that the confidence intervals tend more accurate coverage probabilities than other existin...

Interval-censored data occur naturally in many fields and the main feature is that the failure time of interest is not observed exactly, but is known to fall within some interval. In this paper, we propose a semiparametric probit model for analyzing case 2 interval-censored data as an alternative to the existing semiparametric models in the literature. Specifically, we propose to approximate th...

We derive an identity for nonparametric maximum likelihood estimators (NPMLE) and regularized MLEs in censored data models which expresses the standardized maximum likelihood estimators in terms of the standardized empirical process. This identity provides an effective starting point in proving both consistency and efficiency of NPMLE and regularized MLE. The identity and corresponding method f...

The empirical likelihood ratio method is a general nonparametric inference procedure that has many desirable properties. Recently, the procedure has been generalized to several settings including testing of weighted means with right censored data. However, the computation of the empirical likelihood ratio with censored data and other complex settings is often non-trivial. We propose to use a se...

Water quality studies often include the analytical challenge of incorporating censored data and quantifying error of estimation. Many analytical methods exist for estimating distribution parameters when censored data are present. This paper presents a Bayesian-based hierarchical model for estimating the national distribution of the mean concentrations of chemicals in US public drinking water sy...

We propose a unified strategy for estimator construction, selection, and performance assessment in the presence of censoring. This approach is entirely driven by the choice of a loss function for the full (uncensored) data structure and can be stated in terms of the following three main steps. (1) First, define the parameter of interest as the minimizer of the expected loss, or risk, for a full...

Traditional inferential procedures often fail with censored and truncated data, especially when sample sizes are small. In this paper we evaluate the performances of the double and single bootstrap interval estimates by comparing the double percentile (DB-p), double percentile-t (DB-t), single percentile (B-p), and percentile-t (B-t) bootstrap interval estimation methods via a coverage probabil...