Kernel Ridge Regression with Randomly 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

Prediction from randomly right censored data

Let X be a random vector taking values in IR d , let Y be a bounded random variable, and let C be a right censoring random variable operating on Y. It is assumed that C is independent of (X; Y), the distribution function of C is continuous and the support of the distribution of Y is a proper subset of the support of the distribution of C. Given a sample fX i ; minfY i ; C i g; I Y i C i ] g and...

Estimating Predictive Variances with Kernel Ridge Regression

In many regression tasks, in addition to an accurate estimate of the conditional mean of the target distribution, an indication of the predictive uncertainty is also required. There are two principal sources of this uncertainty: the noise process contaminating the data and the uncertainty in estimating the model parameters based on a limited sample of training data. Both of them can be summaris...

Kernel Ridge Regression via Partitioning

In this paper, we investigate a divide and conquer approach to Kernel Ridge Regression (KRR). Given n samples, the division step involves separating the points based on some underlying disjoint partition of the input space (possibly via clustering), and then computing a KRR estimate for each partition. The conquering step is simple: for each partition, we only consider its own local estimate fo...