Applications of Robust Distances for Regression

Two Robust Fuzzy Regression Models and Their Applications in Predicting Imperfections of Cotton Yarn

The Distribution of Robust Distances

Mahalanobis-type distances in which the shape matrix is derived from a consistent, high-breakdown robust multivariate location and scale estimator have an asymptotic chisquared distribution as is the case with those derived from the ordinary covariance matrix. For example, Rousseeuw’s minimum covariance determinant (MCD) is a robust estimator with a high breakdown. However, even in quite large ...

Econometric applications of high - breakdown robust regression techniques

A literature search shows that robust regression techniques are rarely used in applied econometrics. We present a technique based on Rousseeuw and Van Zomeren [Journal of the American Statistical Association, 85 (1990) 633–639] that removes many of the difficulties in applying such techniques to economic data. We demonstrate the value of these techniques by re-analyzing three OLS-based regressi...

Label-Noise Robust Logistic Regression and Its Applications

The classical problem of learning a classifier relies on a set of labelled examples, without ever questioning the correctness of the provided label assignments. However, there is an increasing realisation that labelling errors are not uncommon in real situations. In this paper we consider a label-noise robust version of the logistic regression and multinomial logistic regression classifiers and...

Robust methods for heteroskedastic regression

Heteroskedastic regression data are modelled using a parameterized variance function. This procedure is robustified using a method with high breakdown point and high efficiency, which provides a direct link between observations and the weights used in model fitting. This feature is vital for the application, the analysis of international trade data from the European Union. Heteroskedasticity is...