Ricardo Fraiman

发表

Semi-supervised learning: When and why it works pdf

Alejandro Cholaquidis, Ricardo Fraiman, Mariela Sued, 2018, ArXiv.

The random projection method in goodness of fit for functional data

Juan Antonio Cuesta-Albertos, Carlos Matrán, Ricardo Fraiman, 2007, Comput. Stat. Data Anal..

On projection-based tests for directional and compositional data

Juan Antonio Cuesta-Albertos, Ricardo Fraiman, Antonio Cuevas, 2009, Stat. Comput..

Random average shifted histograms

Ricardo Fraiman, Badih Ghattas, Mathias Bourel, 2014, Comput. Stat. Data Anal..

Nonparametric likelihood based estimation for a multivariate Lipschitz density

Ricardo Fraiman, Pablo Groisman, Daniel Carando, 2009, J. Multivar. Anal..

Feature selection for functional data

Ricardo Fraiman, Yanina Gimenez, Marcela Svarc, 2015, J. Multivar. Anal..

Resistant estimates for high dimensional and functional data based on random projections

Ricardo Fraiman, Marcela Svarc, R. Fraiman, 2011, Comput. Stat. Data Anal..

Consistent Nonparametric Regression for Functional Data Under the Stone–Besicovitch Conditions

Ricardo Fraiman, Liliana Forzani, Pamela Llop, 2012, IEEE Transactions on Information Theory.

Letter to the editor

Juan Antonio Cuesta-Albertos, Carlos Matrán, Ricardo Fraiman, 2012, J. Multivar. Anal..

Classification methods for functional data

Ricardo Fraiman, Antonio Cuevas, Amparo Baíllo, 2018, Oxford Handbooks Online.

Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data

Ricardo Fraiman, Claudia D. Vargas, Antonio Galves, 2020, Scientific Reports.

Robust estimation and classification for functional data via projection-based depth notions

Ricardo Fraiman, Antonio Cuevas, Manuel Febrero-Bande, 2007, Comput. Stat..

Some hypothesis tests based on random projection

Ricardo Fraiman, Leonardo Moreno, Sebastian Vallejo, 2017, Comput. Stat..

On the use of the bootstrap for estimating functions with functional data

Ricardo Fraiman, Antonio Cuevas, Manuel Febrero-Bande, 2006, Comput. Stat. Data Anal..

An anova test for functional data

Ricardo Fraiman, Antonio Cuevas, Manuel Febrero-Bande, 2004, Comput. Stat. Data Anal..

Impartial trimmed means for functional data

Juan Antonio Cuesta-Albertos, Ricardo Fraiman, 2003, Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications.

Variability in functional brain networks predicts expertise during action observation

Mariano Sigman, Ricardo Fraiman, Daniel Fraiman, 2017, NeuroImage.

On local times, density estimation and supervised classification from functional data

Ricardo Fraiman, Liliana Forzani, Pamela Llop, 2011, J. Multivar. Anal..

A nonlinear aggregation type classifier

Alejandro Cholaquidis, Ricardo Fraiman, Pamela Llop, 2015, J. Multivar. Anal..

Interpretable clustering using unsupervised binary trees

Ricardo Fraiman, Badih Ghattas, Marcela Svarc, 2011, Advances in Data Analysis and Classification.

Detecting trends in time series of functional data: A study of Antarctic climate change

Ricardo Fraiman, Regina Y. Liu, Pamela Llop, 2014 .

Corrigendum to "On depth measures and dual statistics. A methodology for dealing with general data" [JMVA 100 (2009) 753-766]

Ricardo Fraiman, Antonio Cuevas, 2012, J. Multivar. Anal..

Connecting pairwise geodesic spheres by depth: DCOPS

Ricardo Fraiman, Fabrice Gamboa, Leonardo Moreno, 2019, J. Multivar. Anal..

Nonparametric statistics of dynamic networks with distinguishable nodes

Ricardo Fraiman, Daniel Fraiman, Nicolas Fraiman, 2014, TEST.

On semi-supervised learning

Alejandro Cholaquidis, Ricardo Fraiman, Mariela Sued, 2018, TEST.

Multivariate and functional robust fusion methods for structured Big Data

Alejandro Cholaquidis, Ricardo Fraiman, Badih Ghattas, 2019, J. Multivar. Anal..

Set estimation from reflected Brownian motion

Alejandro Cholaquidis, Ricardo Fraiman, G'abor Lugosi, 2014, 1411.0433.

Editorial for the Special Issue on Functional Data Analysis and Related Topics

Ricardo Fraiman, Ricardo Cao, Philippe Vieu, 2019, J. Multivar. Anal..

Quantiles for finite and infinite dimensional data

Ricardo Fraiman, Beatriz Pateiro-López, R. Fraiman, 2012, J. Multivar. Anal..

Pattern recognition via projection-based kNN rules

Ricardo Fraiman, Marcela Svarc, Ana Justel, 2010, Comput. Stat. Data Anal..

Robust fusion methods for Big Data

Alejandro Cholaquidis, Ricardo Fraiman, Badih Ghattas, 2017 .

Context tree selection for functional data

Ricardo Fraiman, Claudia D. Vargas, Antonio Galves, 2016 .

Recent advances in functional data analysis and high-dimensional statistics

Ricardo Fraiman, Christian Genest, Ricardo Cao, 2019, J. Multivar. Anal..

Impartial trimmed k-means for functional data

Juan Antonio Cuesta-Albertos, Ricardo Fraiman, J. A. Cuesta-Albertos, 2007, Comput. Stat. Data Anal..

On visual distances for spectrum-type functional data

Alejandro Cholaquidis, Ricardo Fraiman, Antonio Cuevas, 2014, Adv. Data Anal. Classif..

Clustering using Unsupervised Binary Trees: CUBT pdf

Ricardo Fraiman, Badih Ghattas, Marcela Svarc, 2010, ArXiv.

On depth measures and dual statistics. A methodology for dealing with general data

Ricardo Fraiman, Antonio Cuevas, A. Cuevas, 2009, J. Multivar. Anal..

Corrigendum to “Consistent Nonparametric Regression for Functional Data Under the Stone-Besicovitch Conditions” [Nov 12 6697-6708]

Ricardo Fraiman, Liliana Forzani, Pamela Llop, 2014, IEEE Transactions on Information Theory.

An ANOVA approach for statistical comparisons of brain networks

Ricardo Fraiman, Daniel Fraiman, D. Fraiman, 2018, Scientific Reports.

Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data

Noslen Hernández, Ricardo Fraiman, Antonio Galves, 2021, Scientific reports.

Exchangeability and the Cram\'er-Wold theorem

Ricardo Fraiman, Thomas Ransford, Leonardo Moreno, 2021 .

Home range estimation under a restricted sampling scheme

Alejandro Cholaquidis, Ricardo Fraiman, Manuel Hernandez, 2021, 2106.02035.

Universally consistent estimation of the reach

Alejandro Cholaquidis, Ricardo Fraiman, Leonardo Moreno, 2021 .

Density estimation for spatial-temporal models

Ricardo Fraiman, Liliana Forzani, Pamela Llop, 2013 .

Multivariate density estimation by probing depth

Jean Meloche, Ricardo Fraiman, Regina Y. Liu, 1997 .

ON POINCARÉ CONE PROPERTY

Alejandro Cholaquidis, Ricardo Fraiman, Antonio Cuevas, 2014, 1403.5459.

Multivariate L-estimation

Jean Meloche, Ricardo Fraiman, Alfonso Gordaliza, 1999 .

Nonparametric regression estimation in models with weak error's structure

Ricardo Fraiman, Gonzalo Perez Iribarren, 1991 .

On visual distances in density estimation: the Hausdorff choice

Ricardo Fraiman, Antonio Cuevas, A. Cuevas, 1998 .

Strong Uniform Convergence Rates for some Robust Equivariant Nonparametric Regression Estimates for Mixing Processes

Ricardo Fraiman, Graciela Boente, R. Fraiman, 1991 .

Qualitative Robustness for Stochastic Processes

Ricardo Fraiman, Victor J. Yohai, Graciela Boente, 1987 .

Minimum distance density-based estimation

Ricardo Fraiman, Antonio Cuevas, Ricardo Cao, 1995 .

Conservative Confidence Bands for Nonparametric Regression

Ricardo Fraiman, Gonzalo Perez Iribarren, 1991 .

Counting Bumps

Jean Meloche, Ricardo Fraiman, 1999 .

Asymptotic Distribution of Robust Estimators for Nonparametric Models from Mixing Processes

Ricardo Fraiman, Graciela Boente, R. Fraiman, 1990 .

Asymptotic distribution of data-driven smoothers in density and regression estimation under dependence†

Ricardo Fraiman, Graciela Boente, 1995 .

Selection of Variables for Cluster Analysis and Classification Rules

Ricardo Fraiman, Marcela Svarc, Ana Justel, 2006, math/0610757.

Robust plug-in bandwidth estimators in nonparametric regression

Jean Meloche, Ricardo Fraiman, Graciela Boente, 1997 .

Testing statistical hypothesis on random trees

Ricardo Fraiman, Pablo A. Ferrari, Jorge Rodolfo Busch, 2006 .

Testing statistical hypothesis on random trees and applications to the protein classification problem

Ricardo Fraiman, Ana Georgina Flesia, Jorge R. Busch, 2006, math/0603378.

Robust nonparametric regression estimation

Ricardo Fraiman, Graciela Boente, R. Fraiman, 1989 .

General m-esttmators and applications to bounded influence estimation for non-linear regression

Ricardo Fraiman, 1983 .

On the asymptotic behaviour of general maximum likelihood estimates for the nonregular case under nonstandard conditions

Ricardo Fraiman, Graciela Boente, R. Fraiman, 1988 .

Limit theorems for sequences of random trees

Ricardo Fraiman, Mariela Sued, David Balding, 2004 .