L. Conditionnement-par, 89 3.2.2 Application sur le cas maîtrisé, 93 3.2.4 Conclusions et limites, p.96

.. Modélisation-de-la-dérivée-par-rapport-À-l-'avancement, 98 3.3.2 Résultats sur le cas d'étude

L. 'essai-de-référence, voir annexe C) n'apparaît pas dans cette matrice, mais des essais de contrôle ont été réalisés au cours de la démarche, afin de contrôler que l'échantillon n

.. Filtre-moyen-et-de-savitzky-golay, 174 A.1.1 Filtre moyen (FM), 175 A.1.2 Filtre de Savitzky-Golay (FSG), p.176

.. Le-processus-d-'ornstein-uhlenbeck-conditionné-aux-bords, 186 B.2.1 Représentation du processus d'Ornstein, 186 B.2.2 Processus d'Ornstein-Uhlenbeck conditionné aux bords . . . . . . . . . . . . . . . . . . . . 188

C. Abraham, P. A. Cornillon, E. Matzner-lober, and N. Molinari, Unsupervised curve clustering using b-splines. Rapport technique, ENSAM-INRA-UM II-Montpellier, 2002.

M. Abramowitz, Stegun : Handbook of Mathematical Functions, 1965.

H. Akaike, A new look at the statistical model indentification, IEEE Transactions on Automatic Control, vol.19, pp.719-722, 1974.

H. Akaike, On the likelihood of a time series model. Satistician, pp.215-235, 1978.

S. An, W. Liu, and S. Venkatesh, Face recognition using kernel ridge regression. Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, vol.0, pp.1-7, 2007.

S. Arrhenius, ON THE REACTION VELOCITY OF THE INVERSION OF CANE SUGAR BY ACIDS, Zeitschrift für Physikalische Chemie, vol.4, issue.226, p.1889
DOI : 10.1016/B978-0-08-012344-8.50005-2

H. Ayed, Analyse expérimentale et modélisation du transfert de matière et du mélange dans une couche cisaillée à bulles, Thèse de doctorat, 2007.

L. Bachelier, Th??orie de la sp??culation, Thèse de doctorat, Annales scientifiques de l'École normale supérieure, 1900.
DOI : 10.24033/asens.476

A. B. Hamza, Some properties of relaxed median filters, Proceedings of 13th International Conference on Digital Signal Processing, pp.957-960, 1997.
DOI : 10.1109/ICDSP.1997.628522

P. Besse, H. Cardot, and D. Stephenson, Autoregressive Forecasting of Some Functional Climatic Variations, Scandinavian Journal of Statistics, vol.27, issue.4, pp.673-688, 2000.
DOI : 10.1111/1467-9469.00215

R. Bettinger, Inversion d'un système par krigeage -Application à la synthèse de catalyseurs à haut débit, Thèse de doctorat, 2009.

P. J. Bickel and D. A. Freedman, Some Asymptotic Theory for the Bootstrap, The Annals of Statistics, vol.9, issue.6, pp.1196-1217, 1981.
DOI : 10.1214/aos/1176345637

J. Bigot and S. Gadat, Homeomorphic smoothing splines : a new tool for monotonizing an unconstrained estimator in nonparametric regression, 2007.

N. Brauner and M. Shacham, Statistical analysis of linear and nonlinear correlation of the Arrhenius equation constants, Chemical Engineering and Processing: Process Intensification, vol.36, issue.3, pp.243-249, 1997.
DOI : 10.1016/S0255-2701(96)04186-4

L. Breiman and J. H. Friedman, Estimating Optimal Transformations for Multiple Regression and Correlation, Journal of the American Statistical Association, vol.41, issue.391, pp.580-619, 1985.
DOI : 10.1080/01621459.1985.10478157

L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. , Stone : Classification and Regression Trees, 1984.

B. Brumback and J. Rice, Smoothing Spline Models for the Analysis of Nested and Crossed Samples of Curves, Journal of the American Statistical Association, vol.3, issue.443, pp.961-976, 1998.
DOI : 10.1214/aos/1176349743

Z. Cai, J. Fan, and R. Li, Efficient Estimation and Inferences for Varying-Coefficient Models, Journal of the American Statistical Association, vol.50, issue.451, pp.95888-902, 2000.
DOI : 10.1080/01621459.1996.10476685

Z. Cai, J. Fan, and Q. Yao, Functional-Coefficient Regression Models for Nonlinear Time Series, Journal of the American Statistical Association, vol.10, issue.451, pp.941-956, 2000.
DOI : 10.1007/BF01025869

M. Canaud, F. Wahl, C. Helbert, and L. Carraro, Design of experiments for smoke depollution from the output of diesel engine, ENBIS-EMSE, 2009.

C. Cans and C. Lavergne, De la régression logistique vers un modèle additif généralisé : un exemple d'application. Revue de statistique appliquée, pp.77-90, 1995.

H. Cardot, F. Ferraty, and P. Sarda, Functional linear model, Statistics & Probability Letters, vol.45, issue.1, pp.11-22, 1999.
DOI : 10.1016/S0167-7152(99)00036-X

R. J. Carroll, J. Fan, I. Gijbels, and M. P. , Generalized Partially Linear Single-Index Models, Journal of the American Statistical Association, vol.22, issue.438, pp.477-489, 1997.
DOI : 10.1080/01621459.1997.10474001

R. J. Carroll, D. Ruppert, and A. H. Welsh, Local Estimating Equations, Journal of the American Statistical Association, vol.89, issue.441, pp.214-227, 1998.
DOI : 10.1080/01621459.1998.10474103

R. Chen and R. S. , Tsay : Functional-coefficient autoregressive models, Journal of the American Statistical Association, vol.88, issue.421, pp.298-308, 1993.

C. T. Chiang, J. A. Rice, and C. O. Wu, Smoothing Spline Estimation for Varying Coefficient Models With Repeatedly Measured Dependent Variables, Journal of the American Statistical Association, vol.96, issue.454, pp.605-619, 2001.
DOI : 10.1198/016214501753168280

J. Chilés and P. Delfiner, Geostatistics : modeling spatial uncertainty, 1999.
DOI : 10.1002/9781118136188

W. S. Cleveland and S. J. , Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting, Journal of the American Statistical Association, vol.41, issue.810345, pp.596-610, 1988.
DOI : 10.1080/01621459.1988.10478639

W. S. Cleveland, E. Grosse, and M. J. Shyu, Local regression models éditeurs : Statistical Models in S, pp.309-376, 1992.

B. Conan-guez, Modélisation supervisée de données fonctionnelles par perceptron multi-couches, Thèse de doctorat, 2002.

K. Coussement, D. F. Benoit, and D. , Improved marketing decision making in a customer churn prediction context using generalized additive models, Expert Systems with Applications, vol.37, issue.3, pp.2132-2143, 2010.
DOI : 10.1016/j.eswa.2009.07.029

URL : https://hal.archives-ouvertes.fr/halshs-00581701

P. Craven and G. Whaba, Smoothing noisy data with spline functions, Numerische Mathematik, vol.4, issue.4, pp.377-403, 1979.
DOI : 10.1007/BF01404567

N. Cressie, The origins of kriging, Mathematical Geology, vol.2, issue.3, pp.239-252, 1990.
DOI : 10.1007/BF00889887

I. D. Currie, M. Durban, and P. H. , Eilers : Using p-splines to extrapolate two-dimensional poisson data, Proceedings of 18th International Workshop on Statistical Modelling, pp.97-102, 2003.

C. Currin, T. Mitchell, M. Morris, and D. Ylvisaker, Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer Experiments, Journal of the American Statistical Association, vol.15, issue.416, pp.953-963, 1991.
DOI : 10.1093/biomet/64.2.309

S. Da and V. , Analyse d'incertitudes et de sensibilité -Application aux modèles de cinétique chimique, Thèse de doctorat, 2007.

A. C. Davison and D. V. Hinkley, Bootstrap Methods and their Application, 1997.
DOI : 10.1017/CBO9780511802843

D. Den-hertog, J. P. Kleijnen, and A. Y. , Siem : The correct kriging variance estimated by bootstrapping . Center Discussion Paper No, 2004.

H. Dette and R. Scheder, Strictly monotone and smooth nonparametric regression for two or more variables, Canadian Journal of Statistics, vol.73, issue.4, pp.535-561, 2006.
DOI : 10.1002/cjs.5550340401

D. Dupuy, C. Helbert, and J. Franco, Dicedesign and diceeval : new r packages for design and analysis of computer experiments, 2010.

B. Efron, Bootstrap Methods: Another Look at the Jackknife, The Annals of Statistics, vol.7, issue.1, pp.1-26, 1979.
DOI : 10.1214/aos/1176344552

B. Efron and R. J. , Tibshirani : An Introduction to the Bootstrap, 1993.

P. H. Eilers and B. D. Marx, Flexible smoothing with B -splines and penalties, Statistical Science, vol.11, issue.2, pp.89-121, 1996.
DOI : 10.1214/ss/1038425655

P. H. Eilers and B. D. Marx, Multivariate calibration with temperature interaction using two-dimensional penalized signal regression. hemometrics and, pp.159-174, 2003.

A. Einstein, Ueber die von der molekularkinetischen theorie der wärme gefordete bewegung, Annalen der Physik, vol.17, pp.549-560, 1905.

A. Habachi, Planification d'expériences adaptée aux problèmes de cinétique. applications à la dépollution des fumées en sortie de moteurs. Rapport de stage de l'université Jean Monnet -Saint-Étienne, 2007.

N. Karoui and E. Gobet, Les outils stochastiques des marchés financiers : Une visite guidée de Einstein à Black-Scholes, 2011.

M. Everingham and A. Zisserman, Regression and Classification Approaches to Eye Localization in Face Images, 7th International Conference on Automatic Face and Gesture Recognition (FGR06), pp.441-448, 2006.
DOI : 10.1109/FGR.2006.90

J. Fan and I. Gijbels, Data-driven bandwidth selection in local polynomial fitting : Variable bandwidth and spatial adaptation, Journal of the Royal Statistical Society. Series B (Methodological), vol.57, issue.2, pp.371-394, 1995.

J. Fan, Gijbels : Local polynomial modelling and its applications, 1996.

J. Fan, W. Hardle, and E. Mammen, Direct estimation of additive and linear components for high dimensionnal data. The Annals of statistics, pp.943-971, 1998.

J. Fan, Q. Yao, and Z. Cai, Adaptive varying-coefficient linear models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.94, issue.1, pp.57-80, 2003.
DOI : 10.1006/jmva.1999.1883

J. Fan and J. T. , Two-step estimation of functional linear models with applications to longitudinal data, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.62, issue.2, pp.303-322, 2000.
DOI : 10.1111/1467-9868.00233

J. Fan and W. Zhang, Statistical estimation in varying-coefficient models. The Annals of Statistics, pp.1491-1518, 1999.

K. T. Fang and R. Li, Sudjinato : Design and Modeling for computer experiments, 2005.

L. Favergeon, M. Pijolat, F. Valdivieso, and C. , Experimental study and Monte-Carlo simulation of the nucleation and growth processes during the dehydration of Li2SO4??H2O single crystals, Physical Chemistry Chemical Physics, vol.59, issue.103, pp.3723-3727, 2005.
DOI : 10.1039/b507644g

URL : https://hal.archives-ouvertes.fr/hal-00128100

F. Ferraty and P. Vieu, Nonparametric functional data analysis : methods, theory, applications and implementations, 2006.

R. A. Fischer, The Arrangement of Field Experiments, Journal of the Ministry of Agriculture of Great Britain, vol.33, pp.503-513, 1926.
DOI : 10.1007/978-1-4612-4380-9_8

R. A. Fischer, A System of Scoring Linkage Data, with Special Reference to the Pied Factors in Mice, The American Naturalist, vol.80, issue.794, pp.568-578, 1946.
DOI : 10.1086/281475

A. I. Forrester, A. J. Keane, and N. W. Bressloff, Design and Analysis of "Noisy" Computer Experiments, AIAA Journal, vol.44, issue.10, pp.2331-2339, 2006.
DOI : 10.2514/1.20068

J. Franco, Planification d'expériences numériques en phase exploratoire pour des codes de calculs simulant des phénomènes complexes, Thèse de doctorat, École Nationale Supérieure des Mines de Saint-Etienne, 2008.

J. Franke, J. P. Kreiss, and E. Mammen, Bootstrap of kernel smoothing in nonlinear time series, Bernoulli, vol.8, issue.1, pp.1-37, 2002.

D. A. Freedman, Bootstrapping regession models. The Annals of Statistics, pp.1218-1228, 1981.

J. Friedman, Multivariate adaptive regression splines (with discussion) The Annals of Statistics, pp.1-141, 1991.

J. Gadsby, F. J. Long, P. Sleightholm, and K. W. , The Mechanism of the Carbon Dioxide-Carbon Reaction, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.193, issue.1034, pp.357-376, 1034.
DOI : 10.1098/rspa.1948.0051

C. Gaetan and X. Guyon, Modélisation et statistique spatiales, 2008.

A. Georghiades, P. Belhumeur, and D. Kriegman, From few to many: illumination cone models for face recognition under variable lighting and pose, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.6, pp.643-660, 2005.
DOI : 10.1109/34.927464

D. Ginsbourger, Métamodèles Multiples pour l'Approximation et l'Optimisation de Fonctions Numériques Multivariables, Thèse de doctorat, École Nationale Supérieure des Mines de Saint-Etienne, 2009.

P. J. Green and B. W. Silverman, Nonparametric Regression and Generalized Linear Models : a Roughness Penalty Approach, 1994.
DOI : 10.1007/978-1-4899-4473-3

C. Gu and G. Wahba, Smoothing spline anova with component-wise bayesian confidence intervals, J. Comput. Graph. Statist, vol.2, pp.97-117, 1993.

M. Gunzburger and J. Burkardt, Uniformity measures for point sample un hypercubes, 2004.

P. Hall and J. L. Horowitz, Methodology and convergence rates for functional linear regression, The Annals of Statistics, vol.35, issue.1, pp.70-91, 2007.
DOI : 10.1214/009053606000000957

W. Hardle, J. L. Horowitz, and J. P. Kreiss, Bootstrap Methods for Time Series, International Statistical Review, vol.42, issue.Pt. 2, pp.435-459, 2003.
DOI : 10.1111/j.1751-5823.2003.tb00485.x

T. Hastie, GAM : Generalized Additive Models, 2009.

T. Hastie and R. Tibshirani, Generalized Additive Models, Statistical Science, vol.1, issue.3, pp.297-318, 1986.
DOI : 10.1214/ss/1177013604

T. Hastie and R. Tibshirani, Generalized Additive Models, 1990.

T. Hastie and R. Tibshirani, Varying-coefficient models, Journal of the Royal Statistical Society. Series B (Methodological), vol.55, issue.4, pp.757-796, 1993.

T. Hastie and R. Tibshirani, The elements of statistical learning : data mining, inference and prediction, 2008.

S. Haykin, Adaptive Filter Theory, 2002.

A. E. Hoerl and R. W. Kennard, Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, vol.24, issue.1, pp.55-77, 1970.
DOI : 10.2307/1909769

D. R. Hoover, C. O. Rice, J. A. Wu, and L. P. Yang, Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data, Biometrika, vol.85, issue.4, pp.809-822, 1998.
DOI : 10.1093/biomet/85.4.809

J. L. Horowitz, Bootstrap methods in econometrics : theory and numerical performance Advances in Economics and Econometrics : Theory and Application, pp.188-222, 1997.

J. Z. Huang, C. O. Wu, and L. Zhou, Varying-coefficient models and basis function approximations for the analysis of repeated measurements, Biometrika, vol.89, issue.1, pp.111-128, 2002.
DOI : 10.1093/biomet/89.1.111

C. Jiang and E. B. , Martin : Functional data analysis for the development of a calibration model for near-infrared data, 18th European Symposium on Computer Aided Process Engineering, pp.683-688, 2008.

M. E. Johnson, L. M. Moore, and D. Ylvisaker, Minimax and maximin distance designs, Journal of Statistical Planning and Inference, vol.26, issue.2, pp.131-148, 1990.
DOI : 10.1016/0378-3758(90)90122-B

J. R. Koehler and A. B. Owen, Computer experiments. Handbook of statistics, pp.261-308, 1996.

J. Kowalik and M. R. Osborne, Methods for unconstrained optimization problems, 1968.

S. N. Lahiri, Resampling Methods for Dependent data, 2003.
DOI : 10.1007/978-1-4757-3803-2

C. Leng, A simple approach for varying-coefficient model selection, Journal of Statistical Planning and Inference, vol.139, issue.7, pp.2138-2146, 2009.
DOI : 10.1016/j.jspi.2008.10.009

R. Li, A. Sudjianto, and Z. Zhang, Modeling Computer Experiments with Functional Response, SAE Technical Paper Series, pp.1661-1666, 2005.
DOI : 10.4271/2005-01-1397

D. V. Lindley, On a measures of the information provided by an experiment. The Annals of Mathematical Statistics, pp.986-1005, 1956.

S. N. Lophaven, H. S. Nielsen, and J. S?ndergaard, Aspect of the matlab toolbox dace. Rapport IMM-REP-2002-13, Informatics and mathematical modelling, DTU, 2002.

S. N. Lophaven, H. S. Nielsen, and J. S?ndergaard, A matlab kriging toolbox, version 2.0. Rapport IMM-REP-2002-12, Informatics and mathematical modelling, DTU, 2002.

Y. Lu and S. Mao, Local Asymptotics for B-Spline Estimators of the Varying Coefficient Model, Communications in Statistics - Theory and Methods, vol.55, issue.5, pp.1119-1138, 2004.
DOI : 10.1214/aos/1024691356

Y. Lu, R. Zhang, and L. , Penalized Spline Estimation for Varying-Coefficient Models, Communications in Statistics - Theory and Methods, vol.47, issue.14, pp.2249-2261, 2008.
DOI : 10.1198/016214502388618861

A. Lynn, E. Smid, M. Eshragi, and N. Caldwell, Woody : Modeling hydraulic regenerative hybrid vehicles using amesim and matlab/simulink, Proc. SPIE, p.5805, 2005.

B. D. Marx and P. H. Eilers, Multidimensional Penalized Signal Regression, Technometrics, vol.47, issue.1, pp.13-22, 2005.
DOI : 10.1198/004017004000000626

G. Matheron, Traité de géostatistique appliquée, Tome I, 1962.

G. Matheron, Principles of geostatistics, Economic Geology, vol.58, issue.8, pp.1246-1266, 1963.
DOI : 10.2113/gsecongeo.58.8.1246

G. Matheron, Traité de géostatistique appliquée, Tome II : Le krigeage, Recherches Géologiques et Minières. B.R.G.M, 1963.

B. Matèrn, Spatial Variation [109] N. Matthess : Détermination des lois cinétiques d'épuration des gaz d'échappement automobile par interprétation des courbes de light-off, Thèse de doctorat, 1986.

N. Matthess, D. Schweich, B. Martin, and F. Castagna, From light-off curves to kinetic rate expressions for three-way catalysts, pp.1-4, 2001.

N. Mccann and M. Maeder, Tutorial: The modelling of chemical processes, Analytica Chimica Acta, vol.647, issue.1, pp.31-39, 2009.
DOI : 10.1016/j.aca.2009.05.013

T. J. Mitchell, An algorithm for the construction of d-optimal experimental designs, Technometrics, vol.16, pp.203-210, 1974.

M. Mizuta and J. Kato, Functional Data Analysis and Its Application, 2007.
DOI : 10.1007/978-3-540-72458-2_28

J. M. Morel and S. Solimini, Variational methods in image segmentation, Progress in Nonlinear Différential Equations and Their Applications, 1995.
DOI : 10.1007/978-1-4684-0567-5

W. J. Morokoff and E. Caflish, Quasi-Random Sequences and Their Discrepancies, SIAM Journal on Scientific Computing, vol.15, issue.6, pp.1251-1279, 1994.
DOI : 10.1137/0915077

M. D. Morris and T. J. Mitchell, Exploratory designs for computational experiments, Journal of Statistical Planning and Inference, vol.43, issue.3, pp.381-402, 1995.
DOI : 10.1016/0378-3758(94)00035-T

M. D. Morris and T. J. Mitchell, Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction, Technometrics, vol.15, issue.3, pp.243-255, 1993.
DOI : 10.1080/00401706.1992.10485229

V. M. Muggeo and G. Ferrara, Fitting generalized linear models with unspecified link function: A P-spline approach, Computational Statistics & Data Analysis, vol.52, issue.5, 2008.
DOI : 10.1016/j.csda.2007.08.011

R. Naar, Modélisation du comportement mécanique du béton par approche multi-physique (couplage chimie-mécanique) : application à la réaction alcali-silice, Thèse de doctorat, 2009.

M. Najim, Modélisation, estimation et filtrage optimal en traitement du signal, 2006.

D. Nerini, Monestiez : A cokriging method for spatial functionnal data with applications in oceanology. First International Workshop on Functional and Operatorial Statistics, 2008.

D. Nerini, P. Monestiez, and C. Manté, Cokriging for spatial functional data, Journal of Multivariate Analysis, vol.101, issue.2, pp.409-418, 2010.
DOI : 10.1016/j.jmva.2009.03.005

URL : https://hal.archives-ouvertes.fr/hal-00743881

H. Niederreiter, Low-discrepancy and low-dispersion sequences, Journal of Number Theory, vol.30, issue.1, pp.51-70, 1987.
DOI : 10.1016/0022-314X(88)90025-X

URL : http://doi.org/10.1016/0022-314x(88)90025-x

J. S. Park, Optimal Latin-hypercube designs for computer experiments, Journal of Statistical Planning and Inference, vol.39, issue.1, pp.95-111, 1994.
DOI : 10.1016/0378-3758(94)90115-5

J. Perrin, Les atomes. Félix Alcan, 1913.
DOI : 10.14375/np.9782369430230

P. Pinchon, Futures évolutions des motorisations dans l'automobile. Annales des mines, 2003.

A. Pintore, P. Speckman, and C. C. , Spatially adaptive smoothing splines, Biometrika, vol.93, issue.1, pp.113-125, 2006.
DOI : 10.1093/biomet/93.1.113

URL : http://biomet.oxfordjournals.org/cgi/content/short/93/1/113

J. S. Racine and C. F. Parmeter, Constrained nonparametric kernel regression : estimation and inference. disponible sur http, 2008.

J. O. Ramsay and B. W. , Silverman : Functional data analysis, 1997.

D. Revuz and M. Yor, Continuous martingales and Brownian motion. Grundlehren der mathematischen Wissenschaften, 1999.

S. Richardson, Probl??mes m??thodologiques dans les ??tudes ??cologiques sant?????environnement, Comptes Rendus de l'Acad??mie des Sciences - Series III - Sciences de la Vie, vol.323, issue.7, pp.611-616, 2000.
DOI : 10.1016/S0764-4469(00)00162-1

B. D. Ripley and F. P. Kelly, Markov Point Processes, Journal of the London Mathematical Society, vol.2, issue.1, pp.188-192, 1977.
DOI : 10.1112/jlms/s2-15.1.188

D. Ruppert and R. J. Carroll, Spatially-adaptive Penalties for Spline Fitting, Australian <html_ent glyph="@amp;" ascii="&"/> New Zealand Journal of Statistics, vol.42, issue.2, pp.205-223, 2000.
DOI : 10.1111/1467-842X.00119

D. Ruppert, S. J. Sheather, and M. P. , Wand : An effective bandwidth selector for local least squares regression, Journal of the American Statistical Association, issue.432, pp.901257-1270, 1995.

C. S. Sampara, E. J. Bisset, and M. Chmielewski, Global Kinetics for Platinum Diesel Oxidation Catalysts, Industrial & Engineering Chemistry Research, vol.46, issue.24, pp.7993-8003, 2007.
DOI : 10.1021/ie070642w

T. J. Santner, B. J. Williams, and W. I. , Notz : The design and analysis of computer experiments, 2003.

C. Saunders, A. Gammerman, and V. Vovk, Ridge regression learning algorithm in dual variables, Proceedings of the 15th International Conference on Machine Learning, pp.515-521, 1998.

A. Savitzky and J. E. Golay, Smoothing and Differentiation of Data by Simplified Least Squares Procedures., Analytical Chemistry, vol.36, issue.8, pp.1627-1639, 1964.
DOI : 10.1021/ac60214a047

D. W. Scott, Multivariate Density Estimation : Theory, Practice ans Visualization, 1992.

C. E. Shannon, A mathematical theory of communication. The Bell System Technical Journal, pp.379-423623, 1948.

J. Shao and D. Tu, The Jackknife and Bootstrap, 1995.
DOI : 10.1007/978-1-4612-0795-5

M. C. Shewry and H. P. Wynn, Maximum entropy sampling, Journal of Applied Statistics, vol.14, issue.2, pp.165-170, 1987.
DOI : 10.2307/2987990

J. Q. Shi and B. Wang, Curve prediction and clustering with mixtures of Gaussian process functional regression models, Statistics and Computing, vol.63, issue.3, pp.267-283, 2008.
DOI : 10.1007/s11222-008-9055-1

J. D. Singer and J. B. Willet, Applied longitudinal data analysis, 2003.
DOI : 10.1093/acprof:oso/9780195152968.001.0001

C. J. Stone, Additive Regression and Other Nonparametric Models, The Annals of Statistics, vol.13, issue.2, pp.689-705, 1985.
DOI : 10.1214/aos/1176349548

URL : http://projecteuclid.org/download/pdf_1/euclid.aos/1176349548

C. J. Stone, M. Hansen, C. Kooperberg, and Y. K. , Truong : Polynomial splines and their tensor products in extended linear modeling, Ann. Statist, vol.25, pp.1371-1470, 1997.

D. J. Strauss, A model for clustering, Biometrika, vol.62, issue.2, pp.467-475, 1975.
DOI : 10.1093/biomet/62.2.467

D. Tapsoba, V. Fortin, F. Anctil, and M. Hache, Apport de la technique du krigeage avec d??rive externe pour une cartographie raisonn??e de l'??quivalent en eau de la neige : Application aux bassins de la rivi??re Gatineau, Canadian Journal of Civil Engineering, vol.32, issue.1, pp.289-297, 2005.
DOI : 10.1139/l04-110

E. Thiémard, Sur le calcul et la majoration de la discrépance à l'origine, Thèse de doctorat, 2000.

G. Tutz and H. Binder, Generalized Additive Modeling with Implicit Variable Selection by Likelihood-Based Boosting, Biometrics, vol.99, issue.4, pp.2961-971, 2006.
DOI : 10.1111/j.1541-0420.2006.00578.x

G. Tutz and S. Petry, Nonparametric estimation of the link function including variable selection. Rapport technique, 2010.

V. Vapnik, The Nature of Statistical Learning, 1995.

E. Vazquez, Modélisation comportementale de systèmes non-linéaires multivariables par méthodes à noyaux et applications, Thèse de doctorat, 2005.

S. E. Voltz, C. R. Morgan, D. Liedermann, and S. M. Jacob, Kinetic Study of Carbon Monoxide and Propylene Oxidation on Platinum Catalysts, Industrial & Engineering Chemistry Product Research and Development, vol.12, issue.4, p.294, 1973.
DOI : 10.1021/i360048a006

G. Wahba, Spline Models for observational data, SIAM, 1990.
DOI : 10.1137/1.9781611970128

T. T. Warnock, Computational investigations of low discrepancy point sets éditeur : Applications of Number Theory to Numerical Analysis, pp.319-343, 1972.

M. West, P. J. Harrison, and H. S. Migon, Dynamic Generalized Linear Models and Bayesian Forecasting, Journal of the American Statistical Association, vol.43, issue.389, pp.73-97, 1985.
DOI : 10.1080/01621459.1985.10477131

N. Wiener, Differential-Space, Journal of Mathematics and Physics, vol.2, issue.1-4, pp.131-174, 1923.
DOI : 10.1002/sapm192321131

S. Wood and N. , GAMs with integrated model selection using penalized regression splines and applications to environmental modelling, Ecological Modelling, vol.157, issue.2-3, pp.157-177, 2002.
DOI : 10.1016/S0304-3800(02)00193-X

S. N. Wood, Generalized Additive Models : an Introduction with R. Chapman and Hall-CRC, Boca Raton, 2006.

C. O. Wu, C. T. Chiang, and D. R. Hoover, Asymptotic Confidence Regions for Kernel Smoothing of a Varying-Coefficient Model with Longitudinal Data, Journal of the American Statistical Association, vol.50, issue.444, pp.1388-1402, 1998.
DOI : 10.1080/01621459.1998.10473800

Y. Xiong, W. Chen, and D. Apley, A non-stationary covariance-based Kriging method for metamodelling in engineering design, International Journal for Numerical Methods in Engineering, vol.128, issue.6, pp.733-756, 2007.
DOI : 10.1002/nme.1969

J. Zhang and K. Ma, Kernel fisher discriminant for texture classification, septembre, 2004.

W. Zhang and S. Y. , Lee : On local polynomial fitting of varying coefficient models. manuscript submitted for publication, 1998.

W. Zhang and S. Y. Lee, Variable Bandwidth Selection in Varying-Coefficient Models, Journal of Multivariate Analysis, vol.74, issue.1, pp.116-134, 2000.
DOI : 10.1006/jmva.1999.1883