The high prevalence of spurious solutions and the disturbing effect of outlying observations in mixture modeling are well known problems that pose serious difficulties for non-expert practitioners of this kind of models in different applied areas. An approach which combines the use of Trimmed...

The presence of clusters in a data set is sometimes due to the existence of certain relations among the measured variables which vary depending on some hidden factors. In these cases, observations could be grouped in a natural way around linear and nonlinear structures and, thus, the problem of...

We introduce a robust estimation procedure that is based on the choice of a representative trimmed subsample through an initial robust clustering procedure, and subsequent improvements based on maximum likelihood. To obtain the initial trimming we resort to the trimmed "k"-means, a simple...

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 these objects are points. However, clusters in a data set may often be due to certain relationships between the...

Trimmed best k-nets were introduced in J. A. Cuesta-Albertos, A. Gordaliza and C. Matrán (1998, Statist. Probab. Lett.36, 401-413) as a robustified L[infinity]-based quantization procedure. This paper focuses on the asymptotics of this procedure. Also, some possible applications are briefly...

Let X = {x1,...,xn} be a sample from a multivariate distribution whose location we wish to estimate. A family of multivariate location estimators based on trimming procedures is obtained. High breakdown properties of these estimators are studied.

The "impartial trimming" methodology in clustering analysis was initially designed (see Cuesta-Albertos et al., 1997) to gain protection against outliers and bridging objects (objects intermediate between clusters). In this work the methodology is applied to best k-nets. We include a study of...