This chapter reviews diagnostic and robust procedures for detecting outliers and other
interesting observations in linear regression. First, we present statistics for detecting single …

We extend the standard mixture of linear regressions model by allowing the mixing
proportions to be modeled nonparametrically as a function of the predictors. This framework …

Since the seminal article by Cook, the usual way to measure the influence of an observation
in a statistical model is to delete the observation from the sample and compute a convenient …

Non-hierarchical clustering methods are frequently based on the idea of forming groups
around 'objects'. The main exponent of this class of methods is the k-means method, where …

We introduce an extension to the mixture of linear regressions model where changepoints
are present. Such a model provides greater flexibility over a standard changepoint …

Abstract In 1993, Hathaway and Bezdek combined switching regressions with fuzzy c-
means (FCM) to create fuzzy c-regressions (FCR). The FCR algorithm had been widely …

Transcription factors and corresponding cis-regulatory elements are considered key
components in gene regulation. We combined biostatistics and bioinformatics tools to …

Mixtures of regressions are a set of mixture models that include covariates in the mixture
formulation. The purpose of this work is to study novel models and procedures regarding …

Recently, an algorithm to detect groups in a dataset that follow different linear patterns was
proposed in [VWZ06]. The algorithm is flexible in the sense that it does not require the …

In this paper we look at the problem where we have two mixed sets of data on the same
scatter plot. We want to calculate linear regression lines for each of the two sets, but first of …