Kernel smoothing as an imputation technique for right-censored data

Kernel Ridge Estimator for the Partially Linear Model under Right-Censored Data

Objective: This paper aims to introduce a modified kernel-type ridge estimator for partially linear models under randomly-right censored data. Such models include two main issues that need to be solved: multi-collinearity and censorship. To address these issues, we improved the kernel estimator based on synthetic data transformation and kNN imputation techniques. The key idea of this paper is t...

Smoothing Spline Regression Estimates for Randomly Right Censored Data

Imputation methods for doubly censored HIV data.

In medical research, it is common to have doubly censored survival data: origin time and event time are both subject to censoring. In this paper, we review simple and probability-based methods that are used to impute interval censored origin time and compare the performance of these methods through extensive simulations in the one-sample problem, two-sample problem and Cox regression model prob...

Application of Epanechnikov kernel smoothing technique in disability data

Statistical data contains noise. Smoothing is used to smooth out these noises and present the data as a meaningful one. Kernel methods are nonparametric smoothing tools that can reveal structural features in the data which may not be possible with a parametric approach. This paper applies Epanechnikov kernel method of data smoothing to smooth out the dropout rates of the children with disabilit...

Kernel-Based Multi-Imputation for Missing Data

A Kernel-Based Nonparametric Multiple imputation method is proposed under MAR (Missing at Random) and MCAR (Missing Completely at Random) missing mechanisms in nonparametric regression settings. We experimentally evaluate our approach, and demonstrate that our imputation performs better than the well-known NORM algorithm.