Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Pascal Massart"'
Publikováno v:
Revstat Statistical Journal, Vol 21, Iss 2 (2023)
Random forests are a powerful learning algorithm. However, when dealing with time series, the time-dependent structure is lost, assuming the observations are independent. We propose some variants of random forests for time series. The idea is to repl
Externí odkaz:
https://doaj.org/article/c73786d8529149a588fa523971d568ac
Publikováno v:
Energies, Vol 14, Iss 8, p 2233 (2021)
The field of electric vehicle charging load modelling has been growing rapidly in the last decade. In light of the Paris Agreement, it is crucial to keep encouraging better modelling techniques for successful electric vehicle adoption. Additionally,
Externí odkaz:
https://doaj.org/article/71641f84040d48b99ff7d347c14e2a5e
Concentration inequalities for functions of independent random variables is an area of probability theory that has witnessed a great revolution in the last few decades, and has applications in a wide variety of areas such as machine learning, statist
Publikováno v:
IEEE Transactions on Smart Grid. 11:1895-1904
The development of smart grid and new advanced metering infrastructures induces new opportunities and challenges for utilities. Exploiting smart meters information for forecasting stands as a key point for energy providers who have to deal with time
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c66222a449da4d37b94fa935c456c224
https://doi.org/10.1109/tsg.2019.2945088
https://doi.org/10.1109/tsg.2019.2945088
Publikováno v:
e-Energy
The need for reliable and accessible electric vehicle (EV) charging data is becoming increasingly important as governments and industries aim to create low-carbon transport systems. Without careful grid management, the security of supply could be com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7fd6bb2db2733e62f71294f38cc2fa7
https://doi.org/10.1145/3447555.3466568
https://doi.org/10.1145/3447555.3466568
Publikováno v:
Energies
Energies, 2021, ⟨10.3390/en14082233⟩
Energies, Vol 14, Iss 2233, p 2233 (2021)
Energies, 2021, ⟨10.3390/en14082233⟩
Energies, Vol 14, Iss 2233, p 2233 (2021)
International audience; The field of electric vehicle charging load modelling has been growing rapidly in the last decade. Various models have been applied to this new industrial problem. In light of the Paris Agreement, it is crucial to keep encoura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::312f85004e129642e030ebcab1b54d0c
https://inria.hal.science/hal-03028375
https://inria.hal.science/hal-03028375
Publikováno v:
Sankhya A
Sankhya A, Springer Verlag, 2017, 79 (2), pp.298-335. ⟨10.1007/s13171-017-0107-5⟩
Sankhya A, 2017, 79 (2), pp.298-335. ⟨10.1007/s13171-017-0107-5⟩
Sankhya A, Springer Verlag, 2017, 79 (2), pp.298-335. ⟨10.1007/s13171-017-0107-5⟩
Sankhya A, 2017, 79 (2), pp.298-335. ⟨10.1007/s13171-017-0107-5⟩
International audience; Estimator selection has become a crucial issue in non parametric estimation. Two widely used methods are penalized empirical risk minimization (such as penalized log-likelihood estimation) or pairwise comparison (such as Lepsk
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16f5aea26fc4e1ed1e7eeb72b8a0672c
https://hal.archives-ouvertes.fr/hal-01346081v2/file/PCO_rev.pdf
https://hal.archives-ouvertes.fr/hal-01346081v2/file/PCO_rev.pdf
Autor:
Claire Lacour, Pascal Massart
Publikováno v:
Stochastic Processes and their Applications
Stochastic Processes and their Applications, Elsevier, 2016, In Memoriam: Evarist Giné, 126 (12), pp.3774-3789. ⟨10.1016/j.spa.2016.04.015⟩
Stochastic Processes and their Applications, 2016, In Memoriam: Evarist Giné, 126 (12), pp.3774-3789. ⟨10.1016/j.spa.2016.04.015⟩
Stochastic Processes and their Applications, Elsevier, 2016, In Memoriam: Evarist Giné, 126 (12), pp.3774-3789. ⟨10.1016/j.spa.2016.04.015⟩
Stochastic Processes and their Applications, 2016, In Memoriam: Evarist Giné, 126 (12), pp.3774-3789. ⟨10.1016/j.spa.2016.04.015⟩
International audience; This paper is concerned with adaptive nonparametric estimation using the Goldenshluger-Lepski selection method. This estimator selection method is based on pairwise comparisons between estimators with respect to some loss func
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89011ec5e8c7f8e0845bbd3730a9bde6
https://hal.archives-ouvertes.fr/hal-01121989
https://hal.archives-ouvertes.fr/hal-01121989
Publikováno v:
Electronic Journal of Statistics
Electronic Journal of Statistics, 2016, 10 (2), pp.44
[Research Report] INRIA. 2015
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2016, 10 (2), pp.44
Electron. J. Statist. 10, no. 2 (2016), 2243-2286
Electronic Journal of Statistics, 2016, 10 (2), pp.44
[Research Report] INRIA. 2015
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2016, 10 (2), pp.44
Electron. J. Statist. 10, no. 2 (2016), 2243-2286
International audience; Distances to compact sets are widely used in the field of Topological Data Analysis for inferring geometric and topological features from point clouds. In this context, the distance to a probability measure (DTM) has been intr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4dc47929234b4a9e1aeba216fe18af47
https://hal.archives-ouvertes.fr/hal-01157551
https://hal.archives-ouvertes.fr/hal-01157551
Autor:
Pascal Massart
Publikováno v:
Annales de l'Institut Henri Poincare (B) Probability and Statistics. 38:991-1007
The optimal coupling between a variable with the Bin(n,1/2) distribution and a normal random variable lies at the heart of the proof of the KMT Theorem for the empirical distribution function. Tusnady's Lemma (published in 1977 in his dissertation an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eee813592aa8f00117b00a6ef5798699
https://doi.org/10.1016/s0246-0203(02)01130-5
https://doi.org/10.1016/s0246-0203(02)01130-5