PATILEA Valentin

• "Learning the regularity of multivariate functional data" (with O. Kassi, N. Klutchnikoff); arXiv:2307.14163v2; 
• "Adaptive functional principal components analysis" (with S. Wang, N. Klutchnikoff); arXiv:2306.16091;
• "A 2-step estimation procedure for semiparametric mixture cure models" (with E. Musta, I. Van Keilegom); arXiv:2207.08237;
• "Adaptive optimal estimation of irregular mean and covariance functions" (with S. Golovkine, N. Klutchnikoff); arXiv:2108.06507;
• "Learning the smoothness of weakly dependent functional times series" (with H. Maissoro, M. Vimond); (abstract here);
• "Conditional Lifetimes: a nonparametric and recursive approach" (with D. Aurouet); (abstract here)

Articles

• V. Patilea,  H. Raïssi (2023+) "Powers correlation analysis of non-stationary illiquid assets", Journal of Financial Econometrics, forthcoming  (link here)
• S. Golovkine, N. Klutchnikoff, V. Patilea (2022) "Learning the smoothness of noisy curves with application to online curve estimation", Electronic Journal of Statistics, Vol. 16, No. 1, 1485-1560.
• Y. Berger, V. Patilea (2022) "A semi-parametric empirical likelihood approach for conditional estimating equations under endogenous selection", Econometrics & Statistics, vol. 24, Pages 151-163.
• S. Golovkine, N. Klutchnikoff, V. Patilea (2022) "Clustering multivariate functional data using unsupervised binary trees", Computational Statistics and Data Analysis, (temporary link) (arxiv:2012.05973)
• E. Musta, V. Patilea, I. Van Keilegom (2022) "A presmoothing approach for estimation in mixture cure models", Bernoulli, vol. 28(4), 2689-2715.
• M. Du Roy de Chaumaray, M. Marbac, V. Patilea (2021) "Wilks' theorem for semiparametric regressions with weakly dependent data", The Annals of Statistics, vol. 49(6), 3228-3254 (arxiv:2006.06350).
• K. Burke, V. Patilea (2021) "Penalized maximum likelihood for cure models", TEST, vol. 30, 693–712.
• M. Hristache, V. Patilea  (2021) "Equivalent models for observables under the assumption of missing at random", Econometrics & Statistics, vol. 20, 153-165.
• V. Patilea, I. Van Keilegom (2020) "A General Approach for Cure Models in Survival Analysis", The Annals of Statistics, vol. 48(4), 2323-2346.
• S. Maistre, V. Patilea (2020) "Testing for the significance of the functional covariates in regression models", Journal of Multivariate Analysis, vol. 179, forthcoming.
• V. Patilea, C. Sanchez-Sellero (2020) "Testing for Lack-of-Fit in Functional Regression Models Against General Alternatives", Journal of Statist. Planning and Inference, vol. 209, 229-251. 
• M. Hristache, V. Patilea (2019) "An equivalence result for moment equations when data are missing at random", Statistical Theory and Related Fields, vol. 3(2), 197-207. 
• S. Maistre, V. Patilea (2019) "Nonparametric model checks of single-index assumptions", Statistica Sinica, vol. 29, 113-138.
• W. Li, V. Patilea (2018) "A dimension reduction approach for conditional Kaplan-Meier estimators", TEST, vol. 27(2), 295-315. (long version here)
• M. Hristache, V. Patilea (2017) "Conditional moment models with data missing at random", Biometrika, vol. 104(3), 735–742. 
• W. Li, V. Patilea (2017) "A new inference approach for single-index models", Journal of Multivariate Analysis, vol. 158, 47–59.
• S. Maistre, P. Lavergne, V. Patilea (2016) "Powerful nonparametric checks of quantile regressions". Journal of Statist. Planning and Inference, vol. 180, 13-29.
• V. Patilea, M. Saumard, C. Sanchez-Sellero (2016) "Testing the predictor effect on a functional response"; Journal of the American Statistical Association, vol. 111(516), 1684-1695.
• M. Hristache, V. Patilea (2016) "Semiparametric Efficiency Bounds for Conditional Moment Restriction Models with Different Conditioning Variables"; Econometric Theory, vol. 32(4), 917-946.
• S. Maistre, P. Lavergne, V. Patilea (2015) "A significance test for covariates in nonparametric regression"; Electronic Journal of Statistics, vol. 9(1), 643-678.
• V. Patilea, H. Raïssi (2014) "Testing for second order dynamics for autoregressive processes in presence of time-varying variance"; Journal of the American Statistical Association, vol. 109(507), 1099-1111.
• P. Lavergne, V. Patilea (2013) "Smooth Minimum Distance Estimation and Testing in Conditional Moment Restrictions Models: Uniform in Bandwidth Theory"; Journal of Econometrics, vol. 177, 47-59.
• V. Patilea, H. Raïssi (2013) "Corrected Portmanteau tests for multivariate autoregressive processes time-varying variance"; Journal of Multivariate Analysis, vol. 116, 190-207.
• K. Yu, B. Wang, V. Patilea (2013) "New estimating equation approaches for parameter estimation and exact confidence intervals: application to lifetime data analysis"; Annals of the Institute of Mathematical Statistics, 65(3), 589-615.
• O. Lopez, I. Van Keilegom, V. Patilea (2013) "Single index regression models in the presence of censoring depending on the covariates"; Bernoulli, 19(3), 721-747.
• H. Raïssi, V. Patilea (2012) "Adaptive Estimation of VAR models with Time-Varying Variance: Application to Testing the VAR Order "; Journal of Statist. Planning and Inference, 142 (11), pp. 2891-2912.
• L. Hervé, J. Ledoux, V. Patilea (2012) "The Berry-Esseen bound of M-estimators for geometrically Markov chains"; Bernoulli,18(2), 703-734.
• H. Shiraishi, H. Ogata, T. Amano, V. Patilea, D. Veredas, M. Taniguchi (2012) "Optimal Portfolios with End-of-Period Target"; Advances in Decision Sciences, article ID 703465, 13 pages.
• P. Lavergne, V. Patilea (2012) "One for all and all for one: Dimension reduction for regression checks"; Journal of Business and Economic Statistics, 30(1), pp. 41-52.
• D. Bohning, H. Holling, V. Patilea (2011) "A limitation of the diagnostic-odds ratio in determining an optimal cut-off value for a continuous diagnostic test"; Statistical Methods in Medical Research, vol. 20(5), pp 561-570.
• V. Patilea, L. Sardet (2009) "The rule of thumb for smoothing with asymmetric kernels"; Annales de l'I.S.U.P. vol 53 – Fascicule 2-3, pp 49-60.
• O. Lopez, V. Patilea (2009) "Nonparametric lack-of-fit tests for parametric mean-regression models with censored data"; Journal of Multivariate Analysis, 100(1), pp. 210-230.
• M. Delecroix, O. Lopez, V. Patilea (2008) "Nonlinear Censored Regression Using Synthetic Data"; Scandinavian Journal of Statistics, 35(2), pp. 248-265.
• D. Karlis, V. Patilea (2008) "Bootstrap confidence intervals in mixtures of discrete distributions"; Journal of Statistical Planning and Inference, 138(8), pp. 2313-2329.
• D. Böhning, V. Patilea (2008) "A Capture-Recapture Approach for Screening Using Two Diagnostic Tests with Availability of Disease Status for the Test-Positives Only"; Journal of the American Statistical Association, vol. 103(481), pp. 212-221.
• P. Lavergne, V. Patilea (2008) "Breaking the curse of dimensionality in nonparametric testing"; Journal of Econometrics, vol 143(1), pp. 103-122.
• D. Karlis, V. Patilea (2007) "Confidence intervals of the hazard rate function for discrete distributions using mixtures"; Computational Statistics & Data Analysis, vol. 51, pp. 5388-5401.
• D. Bohning, W. Seidel, M. Alfo, B. Garel, V. Patilea, G. Walther (2007) "Advances in Mixture Models", éditorial pour le numéro spécial sur les mélanges; Computational Statistics & Data Analysis, vol. 51, pp. 5205-5210.
• V. Patilea (2007) "Semiparametric regression models with applications to scoring: a review"; Communication in Statistics (Theory & Methods), vol 36, pp. 1-13.
• V. Patilea, J.M. Rolin (2006) "Product-limit estimators of the survival function with twice censored data"; Annals of Statistics, vol 34, pp. 925-938.
• V. Patilea, J.M. Rolin (2006) "Product-limit estimators of the survival function for two modified forms of current-status data"; Bernoulli, vol 12, pp. 801-819.
• M. Delecroix, M. Hristache, V. Patilea (2006) "On semiparametric M-estimation in single-index regression"; Journal of Statist. Planning and Inference, vol. 136, pp. 730-769.
• D. Bohning, V. Patilea (2005) "Asymptotic normality in mixtures of discrete distributions"; Scandinavian Journal of Statistics, vol. 32, pp. 115-132.
• E. Renault, S. Pastorello, V. Patilea (2003) "Iterative and Recursive Estimation in Structural Non-Adaptive Models (with discussions)"; Journal of Business and Economic Statistics, vol 21, pp. 449-509.
• V. Patilea (2001) "Convex models, NPMLE and misspecification"; Annals of Statistics, vol. 29, pp. 94-123.
  

Book chapters

• V. Patilea, H. Raïssi (2023) "Orthogonal Impulse Response Analysis in Presence of Time-Varying Covariance". Chapitre dans le livre "Research Papers in Statistical Inference for Time Series and Related Models", Springer. Forthcoming.
• E. Ghysels, E. Renault, O. Torres, V. Patilea (1998) "Nonparametric methods and option pricing", chapitre 13 dans Statistics in Finance, D. Hand and S. Jacka (eds.), Arnold Applications of Statistics, London, pp. 261-282.

Proceedings

• H. Raïssi, V. Patilea (2011) "Adaptive Estimation of VAR models with Time-Varying Variance: Application to Testing the VAR Order", 2011 Joint Statistical Meeting Proceedings (American Statistical Association).
• L. Sardet,V. Patilea (2009) "Nonparametric fine tuning of mixtures: application to non-life insurance claims", dans Advances in Data Analysis, Data Handling and Business Intelligence, (Series: Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Berlin-Heidelberg), Fink, A., Lausen, B., Seidel, W., Ultsch, A. (Eds.) pp. 271-282.
• O. Lopez, V. Patilea (2007) "Synthetic data based nonparametric testing of parametric mean-regression models with censored data", dans Recent advances in stochastic modeling and data analysis (World Sci. Publ., Hackensack, NJ),C.H. Skiadas (Ed.)pp. 259-266.

Preprints

• "Semiparametric inference for partially linear regressions with Box-Cox transformation" (with Daniel Becker, A. Kneip); arxiv:2106.10723;
• "Modified Cox regression with current status data" (with L. Bordes, M.C. Pardo, C. Paroissin); arxiv:2002.10412.
• "A semiparametric single-index estimator for a class of estimating equations". (with W. Li, M. Hristache); arXiv:1608.04244. 
• "Projection-based nonparametric testing for functional covariate effect" (with M. Saumard, C. Sanchez-Sellero) ; arXiv:1205.5578.

PhD students

• Omar Kassi (co-supervision with Mathieu Marbac); September 2022 -
• Hassan Maissoro (co-supervision with Myriam Vimond); October 2021 - 
• Sunny Wang ; October 2021 - 
• Daphne Aurouet ; September 2021 - 
• Guillaume Flament ; May 2021 - 

Former PhD students

• Edouard Genetay (co-supervision with Adrien Saumard); defense May 2022
• Steven Golovkine (co-supervision with Nicolas Klutchnikoff); defense Juin 2021) 
• Clément Vital (co-supervision with Laurent Rouvière; defense July 2016)
• Weiyu Li (defense July 2015)
• Samuel Maistre (co-supervision with Pascal Lavergne; defense September 2014)
• Matthieu Saumard (defense May 2013)
• Laure Sardet (co-supervision with Denis Bosq; defense in November 2010)
• Olivier Lopez (co-supervision with Michel Delecroix; defense Decembre 2007)

Visiting PhD students

• Beatriz Piñeiro Lamas (visiting PhD student from UNIVERSIDADE DA CORUÑA; April-July 2022)
• Daniel Becker (visiting PhD student from Bonn University; September 2016, April to June 2017, March 2018)
• Mercedes Amboage (visiting PhD student from Universidad de Santiago de Compostela; Sept-Dec 2015)

2022/2023

Duration Models (2A Ensai, in English)

Multivariate Time Series (2A Ensai, in English)

High-dimensional Time Series (MSc Statistics for Smart Data; jointly with Jad Beyhum, in English)

Functional Data (3A Ensai, in English)

2020/2022

Duration Models (2A Ensai, in English)

High-dimensional Time Series (MSc Statistics for Smart Data; jointly with Romain Tavenard, in English)

Functional Data (MSc Statistics for Smart Data; jointly with Eftychia Solea, in English)

2018/2020

Modèles de régression (2A Ensai)

Duration Models (2A Ensai, in English)

Modèles avancés d'ingénierie financière (3A Ensai)

High-dimensional Time Series (MSc Statistics for Smart Data; jointly with Lionel Truquet, in English)

Deep Learning (MSc Statistics for Smart Data; jointly with Pavlo Mozharovskyi, in English)

2017/2018

Modèles de régression (2A Ensai; in French)

Modèles avancés d'ingénierie financière (3A Ensai; in English)

High-dimensional Time Series (MSc Statistics for Smart Data; jointly with Lionel Truquet, in English)

2016/2017

Modèles de régression (2A Ensai; in French)

Modèles avancés d'ingénierie financière (3A Ensai; in English)

Regression Models (MSc Big-Data; in English)

Aggregation Methods in Statistics and Combinatorial Complexity (MSc Big-Data; jointly with Pavlo Mozharovskyi, in English)

Office hours (office 270)

Monday, Wednesday and Friday: 17:00 - 18:30

2006 - "Habilitation à diriger des recherches" in Mathematics, University of Rennes 1

1997 - PhD in Statistics, Université catholique de Louvain, Louvain-la-Neuve

1993 - MSc in Mathematical Economics and Econometrics, Université Toulouse I

1989 - MSc in Mathematics, University of Bucharest