| An anova test for functional data A Cuevas, M Febrero, R Fraiman Computational statistics & data analysis 47 (1), 111-122, 2004 | 614 | 2004 |
| Robust principal component analysis for functional data N Locantore, JS Marron, DG Simpson, N Tripoli, JT Zhang, KL Cohen, ... Test 8, 1-73, 1999 | 450 | 1999 |
| Trimmed means for functional data R Fraiman, G Muniz Test 10, 419-440, 2001 | 444 | 2001 |
| Robust estimation and classification for functional data via projection-based depth notions A Cuevas, M Febrero, R Fraiman Computational Statistics 22 (3), 481-496, 2007 | 399 | 2007 |
| On the use of the bootstrap for estimating functions with functional data A Cuevas, M Febrero, R Fraiman Computational statistics & data analysis 51 (2), 1063-1074, 2006 | 257 | 2006 |
| Linear functional regression: the case of fixed design and functional response A Cuevas, M Febrero, R Fraiman Canadian Journal of Statistics 30 (2), 285-300, 2002 | 228 | 2002 |
| A plug-in approach to support estimation A Cuevas, R Fraiman The Annals of Statistics, 2300-2312, 1997 | 182 | 1997 |
| Recent advances in functional data analysis and high-dimensional statistics G Aneiros, R Cao, R Fraiman, C Genest, P Vieu Journal of Multivariate Analysis 170, 3-9, 2019 | 165 | 2019 |
| Cluster analysis: a further approach based on density estimation A Cuevas, M Febrero, R Fraiman Computational Statistics & Data Analysis 36 (4), 441-459, 2001 | 159 | 2001 |
| Estimating the number of clusters A Cuevas, M Febrero, R Fraiman Canadian Journal of Statistics 28 (2), 367-382, 2000 | 157 | 2000 |
| Kernel-based functional principal components G Boente, R Fraiman Statistics & probability letters 48 (4), 335-345, 2000 | 145 | 2000 |
| Universal smoothing factor selection in density estimation: theory and practice D Devroye, J Beirlant, R Cao, R Fraiman, P Hall, MC Jones, G Lugosi, ... Test 6, 223-320, 1997 | 116 | 1997 |
| On depth measures and dual statistics. A methodology for dealing with general data A Cuevas, R Fraiman Journal of Multivariate Analysis 100 (4), 753-766, 2009 | 114 | 2009 |
| Interpretable clustering using unsupervised binary trees R Fraiman, B Ghattas, M Svarc Advances in Data Analysis and Classification 7, 125-145, 2013 | 104 | 2013 |
| A sharp form of the Cramér–Wold theorem JA Cuesta-Albertos, R Fraiman, T Ransford Journal of Theoretical Probability 20 (2), 201-209, 2007 | 103 | 2007 |
| Selection of variables for cluster analysis and classification rules R Fraiman, A Justel, M Svarc Journal of the American Statistical Association 103 (483), 1294-1303, 2008 | 97 | 2008 |
| Impartial trimmed k-means for functional data JA Cuesta-Albertos, R Fraiman Computational Statistics & Data Analysis 51 (10), 4864-4877, 2007 | 94 | 2007 |
| Random projections and goodness-of-fit tests in infinite-dimensional spaces JA Cuesta-Albertos*, R Fraiman, T Ransford** Bulletin of the Brazilian Mathematical Society 37 (4), 477-501, 2006 | 92 | 2006 |
| Multivariate L-estimation R Fraiman, J Meloche, LA García-Escudero, A Gordaliza, X He, ... Test 8, 255-317, 1999 | 92 | 1999 |
| Robust nonparametric regression estimation for dependent observations G Boente, R Fraiman The Annals of Statistics 17 (3), 1242-1256, 1989 | 91 | 1989 |
