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In this paper we address the problem of identifying differences between populations of trees. Besides the theoretical relevance of this problem, we are interested in testing if trees characterizing protein sequences from different families constitute samples of significantly different distributions. In this context, trees are obtained by modelling protein… (More)

- Antonio Cuevas, Manuel Febrero-Bande, Ricardo Fraiman
- Computational Statistics & Data Analysis
- 2004

Given k independent samples of functional data the problem of testing the null hypothesis of equality of their respective mean functions is considered. So the setting is quite similar to that of the classical one-way anova model but the k samples under study consist of functional data. A simple natural test for this problem is proposed. It can be seen as an… (More)

- Antonio Cuevas, Ricardo Fraiman, Alberto Rodŕıguez-Casal, A. RODRÍGUEZ-CASAL
- 2007

The Minkowski content L0(G) of a body G⊂ R represents the boundary length (for d = 2) or the surface area (for d = 3) of G. A method for estimating L0(G) is proposed. It relies on a nonparametric estimator based on the information provided by a random sample (taken on a rectangle containing G) in which we are able to identify whether every point is inside… (More)

Hartigan (1975) defines the number q of clusters in a d-variate statistical population as the number of connected components of the set {f > c}, where f denotes the underlying density function on IR and c is a given constant. Some usual cluster algorithms treat q as an input which must be given in advance. The authors propose a method for estimating this… (More)

- Juan Antonio Cuesta-Albertos, Eustasio del Barrio, Ricardo Fraiman, Carlos Matrán
- Computational Statistics & Data Analysis
- 2007

The possibility of considering random projections to identify probability distributions belonging to parametric families is explored. The results are based on considerations involving invariance properties of the family of distributions as well as on the random way of choosing the projections. In particular, it is shown that if a one-dimensional (suitably)… (More)

- Antonio Cuevas, Ricardo Fraiman
- J. Multivariate Analysis
- 2009

In this paper we introduce two procedures for variable selection in cluster analysis and classification rules. One is mainly oriented to detect the “noisy” non–informative variables, while the other deals also with multicolinearity. A forward–backward algorithm is also proposed to make feasible these procedures in large data sets. A small simulation is… (More)

- Ricardo Fraiman, Marcela Svarc
- Computational Statistics & Data Analysis
- 2013

We propose a new robust estimation method based on random projections that is adaptive and, automatically produces a robust estimate, while enabling easy computations for high or infinite dimensional data. Under some restricted contamination models, the procedure is robust and attains full efficiency. We tested the method using both simulated and real data.

- M. Bourel, Ricardo Fraiman, Badih Ghattas
- Computational Statistics & Data Analysis
- 2014

A new density estimator called RASH, for Random Average Shifted Histogram, obtained by averaging several histograms as proposed in Average Shifted Histograms, is presented. The principal difference between the two methods is that in RASH each histogram is built over a grid with random shifted breakpoints. The asymptotic behavior of this estimator is… (More)

- Juan Antonio Cuesta-Albertos, Ricardo Fraiman
- Data Depth: Robust Multivariate Analysis…
- 2003

In this paper we extend the notion of impartial trimming to a functional data framework, and we obtain resistant estimates of the center of a functional distribution. We give mild conditions for the existence and uniqueness of the functional trimmed means. We show the continuity of the population parameter with respect to the weak convergence of probability… (More)