Screening Outliers in Process Control Regression Data with Uniform Residuals, II

Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals

Abstract Gaussian Process (GP) regression models typically assume that residuals are Gaussian and have the same variance for all observations. However, applications with input-dependent noise (heteroscedastic residuals) frequently arise in practice, as do applications in which the residuals do not have a Gaussian distribution. In this paper, we propose a GP Regression model with a latent variab...

On Gamma Regression Residuals

In this paper, ‎we propose new residuals for gamma regression models, ‎assuming that both mean and shape parameters follow regression structures. The  models are summarized and fitted by applying both classic and Bayesian methods as proposed by Cepeda-Cuervo (2001). The residuals are proposed from properties of the biparametric exponential family of distributions. ‎Simulated an...

Rank regression: an alternative regression approach for data with outliers

Linear regression models are widely used in mental health and related health services research. However, the classic linear regression analysis assumes that the data are normally distributed, an assumption that is not met by the data obtained in many studies. One method of dealing with this problem is to use semi-parametric models, which do not require that the data be normally distributed. But...

Using Recursive Residuals, Calculated on Adaptively-ordered Observations, to Identify Outliers in Linear Regression

A novel support vector regression for data set with outliers

Support vector machine (SVM) is sensitive to the outliers, which reduces its generalization ability. This paper presents a novel support vector regression (SVR) together with fuzzification theory, inconsistency matrix and neighbors match operator to address this critical issue. Fuzzification method is exploited to assign similarities on the input space and on the output response to each pair of...