Weighted LS-SVM Regression for Right Censored Data

A Weighted Generalized Ls–svm

Neural networks play an important role in system modelling. This is especially true if model building is mainly based on observed data. Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they automatically answer certain crucial questions involved by neural network construction. They derive an ‘optimal’ network structure and answer...

A Robust LS-SVM Regression

In comparison to the original SVM, which involves a quadratic programming task; LS–SVM simplifies the required computation, but unfortunately the sparseness of standard SVM is lost. Another problem is that LS-SVM is only optimal if the training samples are corrupted by Gaussian noise. In Least Squares SVM (LS–SVM), the nonlinear solution is obtained, by first mapping the input vector to a high ...

Regularized Weighted Linear Regression for High-dimensional Censored Data

Survival analysis aims at modeling time to event data which occurs ubiquitously in many biomedical and healthcare applications. One of the critical challenges with modeling such survival data is the presence of censored outcomes which cannot be handled by standard regression models. In this paper, we propose a regularized linear regression model with weighted least-squares to handle the surviva...

Smoothing Spline Regression Estimates for Randomly Right Censored Data

Nonparametric regression models for right-censored data using Bernstein polynomials

In some applications of survival analysis with covariates, the commonly used semiparametric assumptions (e.g., proportional hazards) may turn out to be stringent and unrealistic, particularly when there is scientific background to believe that survival curves under different covariate combinations will cross during the study period. We present a new nonparametric regression model for the condit...