#### Filter Results:

- Full text PDF available (10)

#### Publication Year

2005

2017

- This year (1)
- Last 5 years (8)
- Last 10 years (15)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Luis A. Garćıa-Escudero, Alfonso Gordaliza, Carlos Matrán
- 2008

We introduce a new method for performing clustering with the aim of fitting clusters with different scatters and weights. It is designed by allowing to handle a proportion α of contaminating data to guarantee the robustness of the method. As a characteristic feature, restrictions on the ratio between the maximum and the minimum eigenvalues of the groups… (More)

- Luis Angel García-Escudero, Alfonso Gordaliza, Carlos Matrán, Agustín Mayo-Iscar
- Statistics and Computing
- 2011

Two key questions in Clustering problems are how to determine the number of groups properly and measure the strength of group-assignments. These questions are specially involved when the presence of certain fraction of outlying data is also expected. Any answer to these two key questions should depend on the assumed probabilisticmodel, the allowed group… (More)

- Luis Angel García-Escudero, Alfonso Gordaliza, Agustín Mayo-Iscar, R. San Martín
- Computational Statistics & Data Analysis
- 2010

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 doing robust clustering around linear affine subspaces has… (More)

- Luis Angel García-Escudero, Alfonso Gordaliza
- J. Classification
- 2005

Functional data sets appear in many areas of science. Although each data point may be seen as a large finite-dimensional vector it is preferable to think of them as functions, and many classical multivariate techniques have been generalized for this kind of data. A widely used technique for dealing with functional data is to choose a finite-dimensional… (More)

- Luis Angel García-Escudero, Alfonso Gordaliza, Carlos Matrán, Agustín Mayo-Iscar
- Adv. Data Analysis and Classification
- 2010

- Luis Angel García-Escudero, Alfonso Gordaliza, Carlos Matrán, Agustín Mayo-Iscar
- Statistics and Computing
- 2015

The maximum likelihood estimation in the finite mixture of distributions setting is an ill-posed problem that is treatable, in practice, through the EM algorithm. However, the existence of spurious solutions (singularities and non-interesting local maximizers) makes difficult to find sensible mixture fits for non-expert practitioners. In this work, a… (More)

- Luis Angel García-Escudero, Alfonso Gordaliza, Agustín Mayo-Iscar
- Adv. Data Analysis and Classification
- 2014

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 Maximum Likelihood ideas and the imposition of restrictions on… (More)

- Luis Angel García-Escudero, Alfonso Gordaliza, Francesca Greselin, Salvatore Ingrassia, Agustín Mayo-Iscar
- Statistics and Computing
- 2017

A robust estimator for a wide family of mixtures of linear regression is presented. Robustness is based on the joint adoption of the cluster weighted model and of an estimator based on trimming and restrictions. The selected model provides the conditional distribution of the response for each group, as in mixtures of regression, and further supplies local… (More)

- Garćıa-Escudero, Alfonso Gordaliza, Carlos Matrán
- 2007

A new method for performing robust clustering is proposed. The method is designed with the aim of fitting clusters with different scatters and weights. A proportion α of contaminating data points is also allowed. Restrictions on the ratio between the maximum and the minimum eigenvalues of the groups scatter matrices are introduced. These restrictions make… (More)

Consistency and weak limit law for trimmed best k-nets are obtained in a quite general framework that covers the multivariate setting and general k 1: Consitency holds for absolutely continuous distributions without conditions on the moments and without the (arti cial) requirement of a trimming level varying with the sample size as in Cuesta-Albertos,… (More)