Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Angelina Roche"'
Publikováno v:
HAL
Functional Principal Component Analysis is a reference method for dimension reduction of curve data. Its theoretical properties are now well understood in the simplified case where the sample curves are fully observed without noise. However, function
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c61b9bb1a58737588d86b8dcfc8c1bc
https://hal.science/hal-03395522
https://hal.science/hal-03395522
Publikováno v:
Journal of Nonparametric Statistics
Journal of Nonparametric Statistics, American Statistical Association, 2020, 32 (3), pp.535-562. ⟨10.1080/10485252.2020.1789125⟩
Journal of Nonparametric Statistics, American Statistical Association, 2020, 32 (3), pp.535-562. ⟨10.1080/10485252.2020.1789125⟩
International audience; We propose an adaptive estimator for the stationary distribution of a bifurcating Markov Chain onRd. Bifurcating Markov chains (BMC for short) are a class of stochastic processes indexed by regular binary trees. A kernel estim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7de44396763a47628693bd9c31bebb4f
https://hal.archives-ouvertes.fr/hal-03031788
https://hal.archives-ouvertes.fr/hal-03031788
Autor:
Angelina Roche, Gaëlle Chagny
Publikováno v:
Journal of Multivariate Analysis
Journal of Multivariate Analysis, Elsevier, 2016, 146, pp.105--118. 〈10.1016/j.jmva.2015.07.001〉
Journal of Multivariate Analysis, Elsevier, 2016, 146, pp.105--118. ⟨10.1016/j.jmva.2015.07.001⟩
Journal of Multivariate Analysis, Elsevier, 2016, 146, pp.105--118. 〈10.1016/j.jmva.2015.07.001〉
Journal of Multivariate Analysis, Elsevier, 2016, 146, pp.105--118. ⟨10.1016/j.jmva.2015.07.001⟩
International audience; In this paper, we consider nonparametric regression estimation when the predictor is a functional random variable (typically a curve) and the response is scalar. Starting from a classical collection of kernel estimates, the bi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8763f7f5aecb6a6beaa7a2ed3c34a546
https://doi.org/10.1016/j.jmva.2015.07.001
https://doi.org/10.1016/j.jmva.2015.07.001
Autor:
Angelina Roche
Publikováno v:
Computational Statistics
Computational Statistics, Springer Verlag, 2018, 33 (1), pp.467-485. ⟨10.1007/s00180-017-0751-1⟩
Computational Statistics, Springer Verlag, In press, 〈10.1007/s00180-017-0751-1〉
Computational Statistics, Springer Verlag, 2018, 33 (1), pp.467-485. ⟨10.1007/s00180-017-0751-1⟩
Computational Statistics, Springer Verlag, In press, 〈10.1007/s00180-017-0751-1〉
International audience; Black-box optimization problems when the input space is a high-dimensional space or a function space appear in more and more applications. In this context, the methods available for finite-dimensional data do not apply. The ai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fdb65bf0b0aa7871db458dd75b67849
https://hal.archives-ouvertes.fr/hal-01144628
https://hal.archives-ouvertes.fr/hal-01144628
Publikováno v:
Journal of Multivariate Analysis
Journal of Multivariate Analysis, Elsevier, 2016, 143, pp.208-232. ⟨10.1016/j.jmva.2015.09.008⟩
Journal of Multivariate Analysis, Elsevier, 2016, 143, pp.208-232. ⟨10.1016/j.jmva.2015.09.008⟩
Functional linear regression has recently attracted considerable interest. Many works focus on asymptotic inference. In this paper we consider in a non asymptotic framework a simple estimation procedure based on functional Principal Regression. It re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56621b9b35d5cbcb1849c87304dafa1a
https://doi.org/10.1016/j.jmva.2015.09.008
https://doi.org/10.1016/j.jmva.2015.09.008
Publikováno v:
Journal of Statistical Planning and Inference
Journal of Statistical Planning and Inference, Elsevier, 2017, 184, pp.25-47. ⟨10.1016/j.jspi.2016.11.002⟩
MAP5 2016-24. 2016
Journal of Statistical Planning and Inference, Elsevier, 2017, 184, pp.25-47. 〈10.1016/j.jspi.2016.11.002〉
Journal of Statistical Planning and Inference, Elsevier, 2017, 184, pp.25-47. ⟨10.1016/j.jspi.2016.11.002⟩
MAP5 2016-24. 2016
Journal of Statistical Planning and Inference, Elsevier, 2017, 184, pp.25-47. 〈10.1016/j.jspi.2016.11.002〉
International audience; We propose an adaptive estimation procedure of the hazard rate of a random variable XX in the multiplicative censoring model, Y=XU, with U∼U([0,1]) independent of X. The variable X is not directly observed: an estimator is b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23ebda697a70c36236b582ec51ae747c
https://hal.archives-ouvertes.fr/hal-01452842
https://hal.archives-ouvertes.fr/hal-01452842
Adaptive and minimax estimation of the cumulative distribution function given a functional covariate
Autor:
Gaëlle Chagny, Angelina Roche
Publikováno v:
Electronic Journal of Statistics
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2014, 8 (2), pp.2352-2404. ⟨10.1214/14-EJS956⟩
Electron. J. Statist. 8, no. 2 (2014), 2352-2404
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2014, 8 (2), pp.2352-2404. ⟨10.1214/14-EJS956⟩
Electron. J. Statist. 8, no. 2 (2014), 2352-2404
International audience; We consider the nonparametric kernel estimation of the conditional cumulative distribution function given a functional covariate. Given the bias-variance trade-off of the risk, we first propose a totally data-driven bandwidth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49b01c4d6ba21d384c554744574d6e90
https://hal.archives-ouvertes.fr/hal-00931228
https://hal.archives-ouvertes.fr/hal-00931228