NAVARRO Fabien

Preprints

• B. de Loynes, F. Navarro, B. Olivier.
  LocLets: Localized Graph Wavelets for Processing Frequency Sparse Signals on Graphs. [arXiv]

• M. du Roy de Chaumaray, M. Marbac, F. Navarro.
  Mixture of hidden Markov models for accelerometer data. [arXiv]

• B. de Loynes, F. Navarro, B. Olivier.
  Data-driven thresholding in denoising with spectral graph wavelet transform. [arXiv]

• A. Saumard, F. Navarro.
  Finite sample improvement of Akaike's Information Criterion.[nbsp][arXiv]

• C. Chesneau, S. El Kolei, F. Navarro.
  Parametric estimation of hidden Markov models by least squares type estimation and deconvolution.[Hal]

Book Translation

• F. Navarro, S. El Kolei, B. Guedj, C. Chesneau, N. Bousquet et al.
  L'apprentissage profond. A French translation of the book:
  Deep Learning, I. Goodfellow, Y. Bengio, A. Courville, MIT Press, 2016.
  Florent Massot [&] Quantmetry, 2018.

Journal Articles

• F. Navarro, A. Saumard.
  Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases
  ESAIM: Probability and Statistics, Vol. 21, 2017. [arXiv][Journal][matlab code]

• C. Chesneau and F. Navarro.
  On the pointwise mean squared error of a multidimensional term-by-term thresholding wavelet estimator
  Communications in Statistics - Theory and Methods, Vol. 46(11), 2017. [Hal][Journal]

• C. Chesneau, F. Navarro , O.S. Serea.
  A note on the adaptive estimation of the differential entropy by wavelet methods
  Comment.Math.Univ.Carolin., Vol. 58(1), 2017. [Hal]

• Y. P. Chaubey, C. Chesneau, F. Navarro.
  Linear wavelet estimation of the derivatives of a regression function based on biased data
  Communications in Statistics - Theory and Methods, Vol. 46(19), 2017. [Hal][Journal]

• I. Bulla, C. Chesneau, F. Navarro , T. Mark.
  A note on the adaptive estimation of a bi-dimensional density in the case of knowledge of the copula density
  Statistics and Probability Letters, Vol. 105, 2015. [Hal][Journal]

• C. Chesneau, F. Comte, G. Mabon, F. Navarro.
  Estimation of convolution in the model with noise
  Journal of Nonparametric Statistics, Vol. 27(3), 2015. [Hal][Journal]

• F. Navarro, C. Chesneau, J. Fadili.
  On adaptive wavelet estimation of a class of weighted densities
  Communications in Statistics - Simulation and Computation, Vol. 44(8), 2015. [Hal][Journal]

• C. Chesneau, M. Kachour, F. Navarro.
  Average Derivative Estimation from Biased Data
  ISRN Probability and Statistics, Vol. 2014, Article ID 864530, 7 pages, 2014. [Journal]

• C. Chesneau, F. Navarro.
  On a plug-in wavelet estimator for convolutions of densities
  Journal of Statistical Theory and Practice, Vol. 8(4), 2014. [Hal][Journal]

• C. Chesneau, M. Kachour, F. Navarro.
  On the estimation of density-weighted average derivative by wavelet methods under various dependence structures
  Sankhya, Vol. 76(1), pp 48–76, 2014. [Hal][Journal]

• C. Chesneau, F. Comte, F. Navarro.
  Fast nonparametric estimation for convolutions of densities
  The Canadian Journal of Statistics,Vol. 41(4), pp. 617–636, 2013. [Hal][Journal]

• C. Chesneau, M. Kachour, F. Navarro.
  A note on the adaptive estimation of a quadratic functional from dependent observations
  İSTATİSTİK, Vol. 6(1), pp. 10–26, 2013. [Hal][Journal]

• F. Navarro, C. Chesneau, J. Fadili, T. Sassi.
  Block thresholding for wavelet-based estimation of function derivatives from a heteroscedastic multichannel convolution model
  Electronic Journal of Statistics, Vol. 7, pp. 428–453, 2013. [Hal][Journal]

Proceedings of international conferences

• C. Chesneau, J. Kou, F. Navarro.
  Linear wavelet estimation in regression with additive and multiplicative noise.
  [nbsp]to appear in[nbsp]ISNPS 2018.[Hal]

• F. Navarro, A. Saumard.
  Efficiency of the V -fold model selection for localized bases
  ISNPS 2016. Springer Proceedings in Mathematics [&] Statistics, vol 250. Springer, Cham, 2018.[Hal][Proc][matlab code]

• F. Navarro, J. Fadili, C. Chesneau.
  Adaptive parameter selection for block wavelet-thresholding deconvolution
  11th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, 2013. [Hal][Proc]

Proceedings of national conferences

• A. Saumard, F. Navarro.
  Une sur-pénalisation théoriquement fondée du critère AIC
  49èmes Journées de Statistique de la SFdS, Avignon, 2017.[Hal]

• F. Navarro, A. Saumard.
  Sélection optimale de modèles à base localisée en régression hétéroscédastique
  48èmes Journées de Statistique de la SFdS, Montpellier, 2016.[Hal]

Ph.D.

• Estimateurs adaptatifs avec parcimonie structurée, 2013. [Hal]

2017-2018 ENSAI

• CM+TP 3ème année MKT et SID,[nbsp]Support Vector Machine

• CM+TP MSc Statistics for Smart data,[nbsp]Machine learning: Features Selection [&] Regularization Methods

• CM+TP 3ème année GS,[nbsp]Python

• CM+TP 3ème année GS et SV,[nbsp]Modélisation non-linéaire

• TP 1ère année,[nbsp]Statistiques avec R

2016-2017 ENSAI

• CM+TP MSc Big data, Penalized Regression

• CM+TP 3ème année GS, Python

• CM+TP 3ème année GS et SV, Modélisation non-linéaire

• TP 1ère année, Statistiques avec R

2015-2016 ENSAI

• CM+TP 3ème année GS et SV, Modélisation non-linéaire

• TD+TP 2ème année, Statistique computationnelle

• TP 1ère année, Statistiques avec R

• TD 1ère année, Algèbre

2014-2015 Université de Concordia (Fall and Winter)

• CM Undergraduate, Fundamental Mathematics II (MATH209)Ch3   Ch4   Ch5   Ch6

2013-2014 Université de Nantes

• CM+TD L1 MIPC - Polytech Nantes, Mathématiques

• TD L2 Physique, Probabilité pour la Physique

• TD L3 Maths-Eco, Inférence statistique

• TD+TP L2 Maths, Probabilités discrètes

2012-2013 Université de Caen

• TD+TP L3 MASS-Maths, OptimisationTP1    TP2    TP3    TP4    TP5   TP6
  

• TD L2 SEG, Mathématiques pour économistes

• TD L1 Biologie, Mathématiques

2011-2012 Université de Caen

• TP M1 Ingénierie Mathématiques et Mécanique, Optimisation

• TD+TP L3 MASS-Maths, Optimisation

• TP L2 MASS, Optimisation

R package : rwavelet

see package vignette for more details.

Matlab toolbox to perform Optimal model selection in heteroscedastic regression

F. Navarro and A. Saumard.
Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases
ESAIM: Probability and Statistics, Vol. 21, 2017. [arXiv][doi][code]

Matlab toolbox to perform V-fold model selection for localized bases

F. Navarro and A. Saumard.
Efficiency of the V -fold model selection for localized bases
soumis,2017.[Hal][code]