Publications

Preprints, reports and unpublished works

• Cardot, H., Koo, J-Y. (2000). Adaptive Log-Splines for Statistical Linear Inverse Problems with Application to 1D Electrophoresis. Rapport technique Biométrie et Intelligence Artificielle, INRA Toulouse.
• Bergez, J.E., Cardot, H. and F. Garcia (2005). Quantile regression trees for yield prediction using a simulation model. Rapport technique Biométrie et Intelligence Artificielle, INRA Toulouse.
• Brossard, T., Cardot, H., Cavailhès (resp. scientifique) J., Hilal, M., Joly, D. et Wavresky, P. (2006). Le prix du climat et l'attrait du littoral en France : une évaluation à partir des valeurs immobilières et du salaire. Rapport remis au Ministère de l'équipement. 251 p.
• Cavailhès, J., Joly, D., Brossard, T., Cardot, H., Hilal, M., Wavresky, P. (2008). Le climat en France et son Prix. Rapport remis au Ministère de l'Emploi, de la Cohésion Sociale et du Logement. 154 p.
• Cardot, H. and Musolesi, A. (2021). Zero-inflated regression for unobserved effects panel data models and difference-in-differences estimation. SEEDS Working Paper 11/2021.
• Cardot, H. and Musolesi, A. (2023). Partially time invariant panel data regression. Submitted for publication.
• Arsenteva, P., Benadjaoud, M-A., Cardot, H. (2023). Joint clustering with alignment for temporal data in a one-point-per-trajectory setting. Arxiv 2311.10282
• Polina Arsenteva, Olivier Guipaud, Vincent Paget, Morgane Dos Santos, Georges Tarlet, Fabien Milliat, Hervé Cardot, Mohamed Amine Benadjaoud. (2024) Comparing cellular response to two radiation treatments based on key features visualization. bioRxiv
• Arsenteva, P., Benadjaoud, M-A., Cardot, H. (2024). Estimating the linear relation between variables that are never jointly observed: an application in in vivo experiments. Arxiv 2403.00140

Articles - Book chapters - Book reviews

• Bourredjem, A., Cardot, H., Devilliers, H. (2024). Asymptotic confidence interval, sample size formulas and comparison test for the agreement intra-class correlation coefficient in inter-rater reliability studies. Statistics in Medicine, accepted for publication.
• Cardot, H. and Frascolla, C. (2024). Hypothesis testing for Panels of Semi-Markov Processes with parametric sojourn time distributions. Journal of Statistical Planning and Inference, 228, 59-79. (A preliminary version is available here).
• Cardot, H., Mas, A. and P. Sarda (2023). Correction : CLT in functional linear regression models. Probability Theory and Related Fields, 187, 519-522.
• Frascolla, C., Visalli, M., Cardot, H. and Schlich, P. (2023). Indexes of individual repeatability and product discrimination in TDS and TCATA and their statistical inference. Food Quality and Preference, 110, August 2023.
• Peltier, C., Visalli, M., Schlich, P. and Cardot, H. (2023). Analyzing Temporal Dominance of Sensations data with Categorical Functional Data Analysis, Food Quality and Preference, 109, July 2023.
• Frascolla, C., Lecuelle, G., Schlich, P. and Cardot, H. (2022). Two sample tests for Semi-Markov Processes with parametric sojourn time distributions: an application in sensory analysis. Computational Statistics, 37, 2553-2580.
• Cardot, H. and Musolesi, A. (2021). Assessing spillover effects of spatial policies with semiparametric zero-inflated models and random forests. In: Daouia A., Ruiz-Gazen A. (eds). Advances in Contemporary Statistics and Econometrics, Springer, pages 319-338.
  
• Mahieu, B., Schlich, P., Visalli, M. and Cardot, H. (2021). A multiple-response chi-square framework for the analysis of Free-Comment and Check-All-That-Apply data. Food Quality and Preferences, Vol 93, October 2021.
• Cardot, H. and Musolesi, A. (2020). Modeling temporal treatment effects with zero inflated semi-parametric regression models: the case of local development policies in France.  Econometric Reviews, 39, 135-157. A preliminary version is on arXiv:1707.05745
• Cardot, H., De Moliner, A. and Goga, C. (2020). Conditional bias robust estimation of the total of curve data by sampling in a finite population: an illustration on electricity load curves. Journal of Survey Statistics and Methodology. 8, 453-482. A preliminary is on arXiv:1806.09949
• Cardot, H., Lecuelle, G., Schlich, P. and Visalli, M. (2019). Estimating finite mixtures of semi-Markov chains: an application to the segmentation of temporal sensory data. Journal of the Royal Statistical Society, Series C, 68, 1281-1303. A preliminary version is on arXiv:1806.04420
• Cardot, H., De Moliner, A. and Goga, C. (2019). Estimation of total electricity consumption curves by sampling in a finite population when some trajectories are partially unobserved. Canadian Journal of Statistics, 47, 65-89.
  
• Lecuelle, G., Visalli, M., Cardot, H. and Schlich, P. (2018). Modeling temporal dominance of sensations with semi-Markov chains.  Food Quality and Preferences,  67, 59-66.
• Cardot, H. and Degras, D. (2018). Online Principal Component Analysis in High Dimension: Which Algorithm to Choose? International Statistical Review,  86, 29-50. R package : onlinePCA

• Cardot, H., Godichon-Baggioni, A. (2017). Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis.  TEST, 26, 461-480. A preliminary version is available here.
• Cardot, H., Cénac, P. and Godichon-Baggioni, A. (2017). Online estimation of the geometric median in Hilbert spaces : non asymptotic confidence balls.  The Annals of Statistics, 45, 591-614.
• Cardot, H., Goga, C. and Shehzad, A. (2017). Calibration and Partial Calibration on Principal Components when the Number of Auxiliary Variables is Large. Statistica Sinica, 27, 243-260.
• De Moliner, A., Goga, C. and Cardot, H. (2016). Estimation of total electricity consumption curves of small areas by sampling in a finite population. Compstat 2016, Eds Colubi, A., Blanco, A. and Gatu, C., 49-57. 
• Cardot, H., De Moliner, A. and Goga, C. (2015). Estimating with kernel smoothers the mean of functional data in a finite population setting. A note on variance estimation in presence of partially observed trajectories. Statistics and Probability Letters, 99, 156-166.
• Truntzer, C., Mostacci, E., Jeannin, A., Petit, J-M., Ducoroy, P. and H. Cardot (2014). Comparison of classification methods that combine clinical data and high dimensional mass spectrometry data. BMC - Bioinformatics, 15:385.
  
• Benadjaoud, M-A.,  Blanchard, P.,  Schwartz, B.,  Champoudry, J., Bouaita, R., Lefkopoulos, D., Deutsch, E.,  Diallo, I., Cardot, H. and  De Vathaire, F. (2014). Functional Data Analysis in NTCP Modeling: A New Method to Explore the Radiation Dose-Volume Effects. International Journal of Radiation Oncology • Biology • Physics, 90, 654-663.
• Lardin, P., Cardot, H. and Goga, C. (2014). Analyzing large datasets of functional data : a survey sampling point of view.  Journal de la SFdS. 155, 70-94.
• Cardot, H, Goga, C. and Lardin, P. (2014). Variance estimation and asymptotic confidence bands for the mean estimator of sampled functional data with high entropy unequal probability sampling designs.  Scandinavian J. of Statistics, 41, 516-534.

• Cardot, H., Goga, C. and Lardin, P. (2013). Uniform convergence and asymptotic confidence bands for model-assisted estimators of the mean of sampled functional data. Electronic J. of Statistics, 7, 562-596.
  
• Cardot, H., Cénac, P. and Zitt, P-A. (2013). Efficient and fast estimation of the geometric median in Hilbert spaces with an averaged stochastic gradient algorithm. Bernoulli, 19, 18-43.
• Sauder, C., Cardot, H., Disenhaus, C. and Le Cozler, Y. (2013). Non-parametric approaches to the impact of Holstein heifer growth from birth to insemination on their dairy performance at lactation one. The Journal of Agricultural Science, 151, 578-589.
• Joly, D., Cardot, H.  and Schaumberger, A. (2013). Improving spatial temperature estimates by resort to time autoregressive processes. International Journal of Climatology, 33, 2289-2297.
• Cardot, H., Degras, D. and Josserand, E. (2013).  Confidence bands for Horvitz-Thompson estimators using sampled noisy functional data.  Bernoulli, 19, 2067-2097.
• Cardot, H., Dessertaine, A., Goga, C., Josserand, E. et Lardin, P. (2013). Comparison of different sample designs and construction of confidence bands to estimate the mean of functional data: An illustration on electricity consumption.  Survey Methodology/Techniques d'enquêtes, 39, 283-301.
• Cardot, H., Cénac, P. and Zitt, P-A. (2012). Recursive estimation of the conditional geometric median in Hilbert spaces.  Electronic Journal of Statistics, 6, 2535-2562.
  
• Cardot, H., Cénac, P. and Monnez, J-M. (2012). A fast and recursive algorithm for clustering large datasets with k-medians.  Computational Statistics and Data Analysis, 56, 1434-1449.
• Cavailhès, J., Joly, D.,  Cardot, H., Hilal, M., Brossard, T. and  Wavresky, P. (2012). The price of climate: French consumer preferences reveal spatial and individual inequalities. In : Cities and climate change: responding to an urgent agenda, Volume 2. World Bank Urban Development Series,  p. 649-669.
  
• Cardot, H., Josserand, E. (2011). Horvitz-Thompson estimators for functional data: asymptotic confidence bands and optimal allocation for stratified sampling.  Biometrika, 98, 107-118.
  
• Joly, D., Brossard, T., Cardot, H., Cavailhes, J., Hilal, M. and Wavresky, P. (2011). Temperature interpolation based on local information : the example of France. International Journal of Climatology, 31, 2141-2153.
  
• Guyon, D., Guillot, M., Vitasse, Y.,  Cardot, H.,  Hagolle, O., Delzon, S. and  J.-P. Wigneron (2011). Elevational variations in leaf phenology of deciduous broadleaved forests from SPOT/VEGETATION time-series. Remote Sensing of Environment, 115, 615-627.

• Book review for Biometrics, 2010, Vol. 66, 1312-1313. Functional Data Analysis with R and Matlab by Ramsay, J. O., Hooker, G., and Graves, S (2009). Springer, New York.
  
• Cardot, H., Cénac, P., Chaouch, M. (2010). Stochastic Approximation for Multivariate and Functional Median. Compstat 2010, Eds Lechevallier, Y. and Saporta, G. Physica-Verlag, Springer, 421-428.
• Cardot, H., Dessertaine, A., Josserand, E. (2010). Semiparametric models with functional responses in survey sampling setting : model assisted estimation of electricity consumption curves. Compstat 2010, Eds Lechevallier, Y. and Saporta, G.  Physica-Verlag, Springer, 411-420.
• Mostacci, E., Truntzer, C.,  Cardot, H., Ducoroy, P.  (2010).  Multivariate denoising methods combining wavelets and PCA for mass spectrometry data. Proteomics, 10, 2564-2572.
• Cardot, H. (2010). Comments on: Dynamic Relations for Sparsely Sampled Gaussian Processes by H.G. Mueller and W. Yang. TEST, 19, 30-33.
• Cardot, H., Chaouch, M., Goga, C. and C. Labruere (2010). Properties of Design-Based Functional Principal Components Analysis. Journal of Statistical Planning and Inference, 140, 75-91.
• Cardot, H., Johannes, J. (2010). Thresholding Projection Estimators in Functional Linear Models,  Journal of Multivariate Analysis, 101, 395-408.
• Cardot, H., Sarda, P. (2010). Functional Linear Regression. In Handbook of Functional Data Analysis, Ferraty, F. and Romain, Y. (Eds.), Oxford University Press, 21-46.
  
• Joly, D., Brossard, T., Cardot, H., Cavailhès, J., Hilal, M., Wavresky, P. (2009). Les types de climats en France, une construction spatiale. Cybergeo, European Journal of Geography.
• Joly, D., Brossard, T., Cardot, H., Cavailhès, J., Hilal, M., Wavresky, P. (2009). Interpolation par régressions locales : application aux précipitations en France, L'Espace géographique, 38, 157-170.
  
• Besse, P., Cardot, H. (2009). Statistical modeling of functional data. In Data Analysis, Ed. G. Govaert. Digital Signal and Image Processing series, John Wiley and Sons, 149-180.
• Cardot, H., Chaouch, M., Goga, C. and C. Labruere (2008). Functional Principal Components Analysis with Survey Data. In Functional and Operatorial Statistics, Dabo-Niang, S. and Ferraty, F. (Eds.), Physica-Verlag, Heidelberg, 95-102. 
• Cardot, H., Maisongrande, P. and Faivre, R. (2008). Varying-Time Random Effects Models for Longitudinal Data: Unmixing and Temporal Interpolation of Remote Sensing Data, Journal of Applied Statistics, Vol. 35, 827-846.
• Joly, D., Brossard, T., Cardot, H.,  Cavailhès, J., Hilal, M., Wavresky, P.  (2008).  Interpolation par recherche d'information locale. Climatologie, Vol. 5, 27-48.
• Cardot, H. and Sarda, P. (2008). Varying-Coefficient Functional Linear Regression Models. Communications in Statistics: Theory and Methods, Vol. 37, 3186-3203.

• Cardot, H.,  Mas, A. and P. Sarda (2007). CLT in functional linear regression models, Probability Theory and Related Fields, Vol. 138, 325-361.
  
• Cardot, H., Crambes, C., Kneip, A. and Sarda, P. (2007). Smoothing spline estimators in functional linear regression with errors-in-variables, Computational Statistics and Data Analysis, Vol. 51, 4832-4848.
  
• Cardot, H., Prchal, L. and Sarda, P. (2007). No effect and lack-of-fit permutation tests for functional regression. Computational Statistics, Vol. 22, 371-390.
  
• Cardot, H. (2007). Conditional functional principal components analysis. Scandinavian Journal of Statistics, Vol. 34, 317-335.
• Cardot, H., Crambes, C. and P. Sarda (2007). Ozone Pollution Forecasting using Conditional Mean and Conditional Quantiles with Functional Covariates. In Statistical Methods for Biostatistics and Related Fields, Hardle, W., Mori, Y. and P. Vieu (Eds.), Springer Verlag, New York, 221-244.
• Cardot, H., Sarda, P. (2006). Linear Regression Models for functional data. In The Art of Semiparametrics, ed. W. Hardle, Physica-Verlag/Springer, Heidelberg, 49-66.
• Cardot, H., Crambes, C and P. Sarda (2005). Quantile regression when the covariates are functions. Journal of Nonparametric Statistics, Vol. 17, 841-856.
• Cardot, H.,  Sarda, P. (2005). Estimation in Generalized Linear Models for Functional Data via Penalized Likelihood. Journal of Multivariate Analysis, Vol. 92, 24-41.
• Besse, P.C., Cardot, H., Faivre, R. and M. Goulard (2005). Statistical modelling of functional data. Applied Stochastic Models in Business and Industry, Vol. 21, 165-173.

• Cardot, H., Faivre, R. and P. Maisongrande (2004). Random Effects Varying Time Regression Models: Application to Remote Sensing. Compstat 2004 proceedings. ed. J. Antoch, Physica-Verlag, 777-784.
• Cardot, H., Crambes, C. and P.  Sarda (2004). Conditional Quantiles with Functional Covariates : an application to Ozone pollution forecasting. Compstat 2004 proceedings. ed. J. Antoch, Physica-Verlag, 769-776.
• Aneiros-Perez, G., Cardot, H.,  Estevez-Perez G.  and P. Vieu (2004). Maximum ozone concentration forecasting by functional nonparametric approaches. Environmetrics, Vol. 15, 675-685.
• Cardot, H.,  Goia, A. and P. Sarda (2004). Testing for no effect in functional linear regression models, some computational approaches. Communications in Statistics - Simulation and Computation, Vol. 33, 179-199.
• Cardot, H., Koo, J-Y., Park, H.J.  and A. Trubuil (2004). Boosting Diracs for Electrophoresis. Journal of Computational and Graphical Statistics, Vol. 13, 659-673.
• Cardot, H., Crambes, C., Sarda, P. (2004). Estimation spline de quantiles conditionnels pour variables explicatives fonctionnelles. C. R. Acad. Sci. Paris, Série I, 339, 141-144.
• Besse, P.C. et H. Cardot (2003). Modélisation statistique de données fonctionnelles. in Analyse des Données, édité par G. Govaert, Hermes, pages 169-200.
• Cardot, H.,  Faivre, R. and M. Goulard (2003). Functional approaches for predicting land use with the temporal evolution of coarse resolution remote sensing data. Journal of Applied Statistics, Vol. 30, 1185-1199.
• Cardot, H., Ferraty, F. and P. Sarda (2003). Spline Estimators for the Functional Linear Model. Statistica Sinica, Vol. 13, 571-591.
• Cardot, H., Ferraty, F., Mas, A., and P. Sarda (2003). Testing Hypotheses in the Functional Linear Model. Scandinavian Journal of Statistics, Vol. 30, 241-255.

• Cardot, H. (2002). Local roughness penalties for regression splines. Computational Statistics, Vol 17, 89-102.
• Cardot, H. (2002). Spatially Adaptive Splines for Statistical Linear Inverse Problems. Journal of Multivariate Analysis, Vol. 81, 100-119.
• Besse, P.C., Cardot, H., and D. Stephenson (2000). Autoregressive Forecasting of Some Functional Climatic Variations. Scandinavian Journal of Statistics, Vol. 27, 673-687.
• Cardot, H. (2000). Nonparametric Estimation of Smoothed Principal Components Analysis of Sampled Noisy Functions. Journal of Nonparametric Statistics, Vol. 12, 503-538.
• Cardot, H., F. Ferraty, F. et P. Sarda. (2000). Etude asymptotique d'un estimateur spline hybride pour le modèle linéaire fonctionnel. C. R. Acad. Sci. Paris, t. 330, Série I, 501-504.
• Cardot, H., Ferraty, F. and P. Sarda (1999). Functional Linear Model. Statistics and Probability Letters. Vol. 45, 1, 11-22.
• Cardot, H. (1998). Convergence du lissage spline de la prévision des processus autorégressifs fonctionnels. C. R. Acad. Sci. Paris, t. 326, Série I, 755-758.
• Cardot, H., Diack, C. (1998). Convergence en moyenne quadratique de l'estimateur de la régression par splines hybrides. C. R. Acad. Sci. Paris , t. 326, Série I, 615-618.
• Besse, P.C., Cardot, H. and F. Ferraty (1997). Simultaneous nonparametric regressions of unbalanced longitudinal data. Computational Statistics and Data Analysis. Vol. 24, 255-270.
•  Besse, P.C., H. Cardot (1996). Approximation spline de la prévision d'un processus fonctionnel autorégressif d'ordre 1. Canadian Journal of Statistics/ Revue Canadienne de Statistique. Vol. 24, 467-487.