Dimension reduction in functional regression with applications, Computational Statistics & Data Analysis, vol.50, issue.9, pp.2422-2446, 2006. ,
DOI : 10.1016/j.csda.2004.12.007
URL : https://hal.archives-ouvertes.fr/hal-00103266
Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study, Journal of Statistical Software, vol.6, issue.6, pp.1-83, 2001. ,
DOI : 10.18637/jss.v006.i06
URL : https://hal.archives-ouvertes.fr/hal-00823485
Functional Logistic Discrimination Via Regularized Basis Expansions, Communications in Statistics - Theory and Methods, vol.9, issue.16-17, pp.2944-2957, 2009. ,
DOI : 10.1080/03610920902947246
Functional Classification in Hilbert Spaces, IEEE Transactions on Information Theory, vol.51, issue.6, pp.2163-2172, 2005. ,
DOI : 10.1109/TIT.2005.847705
Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996. ,
DOI : 10.1007/BF00058655
Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001. ,
DOI : 10.1023/A:1010933404324
Classification and regression trees, 1984. ,
Prediction in functional linear regression, The Annals of Statistics, vol.34, issue.5, pp.2159-2179, 2006. ,
DOI : 10.1214/009053606000000830
Functional linear model, Statistics & Probability Letters, vol.45, issue.1, pp.11-22, 1999. ,
DOI : 10.1016/S0167-7152(99)00036-X
Spline estimators for the functional linear model, Statistica Sinica, vol.13, pp.571-592, 2003. ,
Selecting Useful Groups of Features in a Connectionist Framework, IEEE Transactions on Neural Networks, vol.19, issue.3, pp.381-396, 2008. ,
DOI : 10.1109/TNN.2007.910730
Sparse Group Lasso: Consistency and Climate Applications, pp.47-58, 2012. ,
DOI : 10.1137/1.9781611972825.5
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.362.4892
Gene selection and classification of microarray data using random forest, BMC Bioinformatics, vol.7, issue.3, 2006. ,
Ideal spatial adaptation by wavelet shrinkage, Biometrika, vol.81, issue.3, pp.425-455, 1994. ,
DOI : 10.1093/biomet/81.3.425
Wavelet shrinkage: asymptopia, Journal of the Royal Statistical Society, Series B, vol.57, pp.301-369, 1995. ,
Functional additive regression, The Annals of Statistics, vol.43, issue.5, 2013. ,
DOI : 10.1214/15-AOS1346SUPP
URL : http://arxiv.org/abs/1510.04064
Recent Advances in Functional Data Analysis and Related Topics, 2011. ,
DOI : 10.1007/978-3-7908-2736-1
URL : https://hal.archives-ouvertes.fr/hal-00794868
Nonparametric Functional Data Analysis: Theory and Practice, 2006. ,
Functional Classification with Margin Conditions, 19th Annual Conference on Learning Theory, 2006. ,
DOI : 10.1007/11776420_10
URL : https://hal.archives-ouvertes.fr/hal-00457770
Variable selection using random forests, Pattern Recognition Letters, vol.31, issue.14, pp.2225-2236, 2010. ,
DOI : 10.1016/j.patrec.2010.03.014
URL : https://hal.archives-ouvertes.fr/hal-00755489
A comparison of tests for the one-way ANOVA problem for functional data, Computational Statistics, vol.14, issue.4, pp.1-24, 2015. ,
DOI : 10.1007/s00180-015-0555-0
Correlation and variable importance in random forests, Statistics and Computing, vol.2, issue.1, pp.1310-5726, 2014. ,
DOI : 10.1007/s11222-016-9646-1
URL : https://hal.archives-ouvertes.fr/hal-00879978
An introduction to variable and feature selection, The Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003. ,
Gene selection for cancer classification using support vector machines, Machine Learning, vol.46, issue.1/3, pp.389-422, 2002. ,
DOI : 10.1023/A:1012487302797
Stable feature selection for biomarker discovery, Computational Biology and Chemistry, vol.34, issue.4, pp.215-225, 2010. ,
DOI : 10.1016/j.compbiolchem.2010.07.002
URL : http://arxiv.org/abs/1001.0887
Adaptive estimation of a quadratic functional of a density by model selection, ESAIM: Probability and Statistics, vol.9, pp.1245-1501, 2000. ,
DOI : 10.1051/ps:2005001
Supervised group Lasso with applications to microarray data analysis, BMC Bioinformatics, vol.8, issue.1, p.60, 2007. ,
DOI : 10.1186/1471-2105-8-60
Variable and boundary selection for functional data via multiclass logistic regression modeling, Computational Statistics & Data Analysis, vol.78, pp.176-185, 2014. ,
DOI : 10.1016/j.csda.2014.04.015
Variable selection for functional regression models via the regularization, Computational Statistics & Data Analysis, vol.55, issue.12, pp.3304-3310, 2011. ,
DOI : 10.1016/j.csda.2011.06.016
The group lasso for logistic regression, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.68, issue.1, pp.53-71, 2008. ,
DOI : 10.1111/j.1467-9868.2007.00627.x
The behaviour of random forest permutationbased variable importance measures under predictor correlation, BMC Bioinformatics, vol.11, issue.110, 2010. ,
Wavelet Methods for Time Series Analysis, 2000. ,
Classification supervisée en grande dimension. applicationàapplicationà l'agrément de conduite automobile, pp.39-58, 2006. ,
Functional Data Analysis, 2005. ,
Linear statistical inference and its applications Wiley series in probability and mathematical statistics: Probability and mathematical statistics, 1973. ,
A Functional Approach to Variable Selection in Spectrometric Problems, Proceedings of 16th International Conference on Artificial Neural Networks, ICANN 2006, pp.11-20, 2006. ,
DOI : 10.1007/11840817_2
Support vector machine for functional data classification, Neurocomputing, vol.69, issue.7-9, pp.730-742, 2006. ,
DOI : 10.1016/j.neucom.2005.12.010
URL : https://hal.archives-ouvertes.fr/hal-00144141
Recent Advances in the Use of SVM for Functional Data Classification, Proceedings of 1rst International Workshop on Functional and Operatorial Statistics, 2008. ,
DOI : 10.1007/978-3-7908-2062-1_41
URL : https://hal.archives-ouvertes.fr/hal-00635480
Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society, Series B, vol.58, pp.267-288, 1996. ,
A supervised feature subset selection technique for multivariate time series, Proceedings of the Workshop on Feature Selection for Data Mining: Interfacing Machine Learning with Statistics, 2005. ,
Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features, Sixth IEEE International Conference on Data Mining, Workshops (ICDMW'06), pp.337-342, 2006. ,
DOI : 10.1109/ICDMW.2006.81
Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.49-67, 2006. ,
DOI : 10.1198/016214502753479356
Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.49-67, 2006. ,
DOI : 10.1198/016214502753479356
Variable selection for the multicategory SVM via adaptive sup-norm regularization, Electronic Journal of Statistics, vol.2, issue.0, pp.149-167, 2008. ,
DOI : 10.1214/08-EJS122
Reinforcement Learning Trees, Journal of the American Statistical Association, vol.7, issue.512, 2012. ,
DOI : 10.1080/01621459.2011.637468
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4760114