PATILEA Valentin

 

Prépublications récentes

• "An information criterion for detecting periodicities in functional time series" (avec R. Sagawa, Y. Liu);  

• "Rate accelerated inference for integrals of multivariate random functions" (avec S. Wang);  

• "Adaptive prediction for functional time series" (with H. Maissoro, M. Vimond);  

• "Locally Adaptive Online Functional Data Analysis" (avec J. Racine); (working paper ici)

• "Learning the smoothness of weakly dependent functional times series" (avec H. Maissoro, M. Vimond); arXiv:2403.13706;

• "Learning the regularity of multivariate functional data" (avec O. Kassi, N. Klutchnikoff); arXiv:2307.14163v2;

• "Conditional Lifetimes: a nonparametric and recursive approach" (avec D. Aurouet); (résumé ici)

 

Articles 

S. Wang, V. Patilea, N. Klutchnikoff (2025+) "Adaptive functional principal components analysis", Journal of the Royal Statistical Society Series B, accepté (arXiv:2306.16091).

V. Patilea, F. Portier (2025+) "Density model checks via the lack-of-fitness", forthcoming (working paper version here)

E. Musta, V. Patilea, I. Van Keilegom (2024+) "Regression estimation using surrogate responses obtained by presmoothing", Statistica Neerlandica, accepté (http://doi.org/10.1111/stan.12351).

E. Musta, V. Patilea, I. Van Keilegom (2024) "A 2-step estimation procedure for semiparametric mixture cure models", Scandinavian Journal of Statistics, Vol. 51, No. 3, 987-1011.

S. Golovkine, N. Klutchnikoff, V. Patilea (2024+) "Adaptive estimation of irregular mean and covariance functions", Bernoulli, accepté.

V. Patilea,  H. Raïssi (2023+) "Powers correlation analysis of non-stationary illiquid assets", Journal of Financial Econometrics, accepté  (lien ici)

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.

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 Statist. 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"; The 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", The Annals of Statistics, vol. 29, pp. 94-123.

 

Chapitres livres

V. Patilea, V., J.M. Rolin (2024) "Smooth Versus Likelihood Estimation for a Class of Mixtures of Discrete Distributions". Chapitre dans le livre "Nonparametric Bayesian Inference", Florens, JP., Mouchart, M. (eds), pp. 315-339, Springer, Cham (https://doi.org/10.1007/978-3-031-61329-6_13).

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", Essays in Honor of Masanobu Taniguchi, Y. Liu, J. Hirukawa, Y. Kakizawa (eds.), pp. 416-443, Springer (https://doi.org/10.1007/978-981-99-0803-5). 

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.

 

Prépublications

"Semiparametric inference for partially linear regressions with Box-Cox transformation" (avec Daniel Becker, A. Kneip); arxiv:2106.10723;

"Modified Cox regression with current status data" (avec L. Bordes, M.C. Pardo, C. Paroissin); arxiv:2002.10412.

"A semiparametric single-index estimator for a class of estimating equations". (avec W. Li, M. Hristache); arXiv:1608.04244, under revision AISM. 

"Projection-based nonparametric testing for functional covariate effect" (avec M. Saumard, C. Sanchez-Sellero) ; arXiv:1205.5578.

FunStatMath is a research initiative on Functional Data Analysis (FDA). FDA has been the subject of growing interest over the last few decades, as many data can naturally be viewed as curves or surfaces rather than vectors in multivariate analysis. The functional framework spans many application areas, such as functional imaging in neuroscience, spatio-temporal data in environmental sciences, and imaging data in molecular and cell biology, etc. As a result, the mathematical challenges posed by FDA have the capacity to influence diverse scientific fields far beyond their mathematical components. The leader of the network is Angelina Roche.

Seminars 2024:

June 14, 2024, Ensai, 11 a.m: Alberto Suarez (UAM) : Machine learning with functional data:  near-perfect classification. Abstract  abstract_Alberto_Suarez

June 21, 2024, Ensai, 2 p.m: Tailen Hsing (University of Michigan) : A functional-data perspective in spatial data analysis. Abstract abstract_Tailen_Hsing

 

Other activities:

Three talks from the members of the network will be given in the Invited Paper Session Adaptive functional data analysis, at ISNPS 2024, June 25-29, 2024, Braga, Portugal