Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Christophe Crambes"'
Dealing with missing values is an important issue in data observation or data recording process. In this paper, we consider a functional linear regression model with partially observed covariate and missing values in the response. We use a reconstruc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d6636d8dc5203c416f944638e212b36
https://hal.archives-ouvertes.fr/hal-03083293
https://hal.archives-ouvertes.fr/hal-03083293
Autor:
Yousri Henchiri, Christophe Crambes
Publikováno v:
Journal of Statistical Planning and Inference
Journal of Statistical Planning and Inference, Elsevier, 2019, 201, pp.103-109. ⟨10.1016/j.jspi.2018.12.004⟩
Journal of Statistical Planning and Inference, Elsevier, 2019, 201, pp.103-109. ⟨10.1016/j.jspi.2018.12.004⟩
International audience; We are interested in functional linear regression when some observations of the real response are missing, while the functional covariate is completely observed. A complete case regression imputation method of missing data is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69afd7279e5382afd4c6ac747fe1d22b
https://hal.archives-ouvertes.fr/hal-01521954
https://hal.archives-ouvertes.fr/hal-01521954
Publikováno v:
Electronic Journal of Statistics 1 (12), 985-1018. (2018)
Electronic Journal of Statistics
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2018, 12 (1), pp.985-1018. ⟨10.1214/18-EJS1412⟩
Electron. J. Statist. 12, no. 1 (2018), 985-1018
Electronic journal of statistics
Electronic journal of statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2018, 12 (1), pp.985-1018. ⟨10.1214/18-EJS1412⟩
Electronic Journal of Statistics
Electronic Journal of Statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2018, 12 (1), pp.985-1018. ⟨10.1214/18-EJS1412⟩
Electron. J. Statist. 12, no. 1 (2018), 985-1018
Electronic journal of statistics
Electronic journal of statistics, Shaker Heights, OH : Institute of Mathematical Statistics, 2018, 12 (1), pp.985-1018. ⟨10.1214/18-EJS1412⟩
International audience; The aim of this paper is to propose estimators of the unknown functional coefficients in the Functional Concurrent Model (FCM). We extend the Ridge Regression method developed in the classical linear case to the functional dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be6c4e649552e484f2f7b7f49088af02
http://prodinra.inra.fr/record/440893
http://prodinra.inra.fr/record/440893
Publikováno v:
Journal of Nonparametric Statistics
Journal of Nonparametric Statistics, American Statistical Association, 2014, 26 (4), pp.639-668. ⟨10.1080/10485252.2014.941365⟩
Journal of Nonparametric Statistics, American Statistical Association, 2014, 26 (4), pp.639-668. ⟨10.1080/10485252.2014.941365⟩
This work deals with conditional quantiles estimation when several functional covariates are involved, via a support vector machines nonparametric methodology. We establish weak consistency of this estimator. To fit the additive components, we use an
Publikováno v:
Statistics and Probability Letters
Statistics and Probability Letters, Elsevier, 2016, 113, pp.7-15. ⟨10.1016/j.spl.2016.02.006⟩
Statistics and Probability Letters (113), 7-15. (2016)
Statistics and Probability Letters, Elsevier, 2016, 113, pp.7-15. ⟨10.1016/j.spl.2016.02.006⟩
Statistics and Probability Letters (113), 7-15. (2016)
This work deals with the estimation of the noise in functional linear regression when both the response and the covariate are functional. Namely, we propose two estimators of the covariance operator of the noise. We give some asymptotic properties of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ab01b3a7c056d0beeb4e0704d90f90c
https://hal.archives-ouvertes.fr/hal-01331242
https://hal.archives-ouvertes.fr/hal-01331242
Publikováno v:
Statistics and Probability Letters
Statistics and Probability Letters, Elsevier, 2011, 81 (12), pp.1847-1858. ⟨10.1016/j.spl.2011.07.008⟩
Statistics and Probability Letters, Elsevier, 2011, 81 (12), pp.1847-1858. ⟨10.1016/j.spl.2011.07.008⟩
This paper deals with a nonparametric estimation of conditional quantile regression when the explanatory variable X takes its values in a bounded subspace of a functional space X and the response Y takes its values in a compact of the space Y≔R. Th
Publikováno v:
Journal of Nonparametric Statistics
Journal of Nonparametric Statistics, American Statistical Association, 2008, 20 (7), pp.573-598. ⟨10.1080/10485250802331524⟩
Journal of Nonparametric Statistics, American Statistical Association, 2008, 20 (7), pp.573-598. ⟨10.1080/10485250802331524⟩
Robust estimation provides an alternative approach to classical methods, for instance, when the data are affected by the presence of outliers. Recently, these robust estimators have been considered...
Publikováno v:
Computational Statistics and Data Analysis
Computational Statistics and Data Analysis, Elsevier, 2014, 69, pp.154-172. ⟨10.1016/j.csda.2013.07.030⟩
Computational Statistics and Data Analysis, Elsevier, 2014, 69, pp.154-172. ⟨10.1016/j.csda.2013.07.030⟩
The main purpose is to estimate the regression function of a real random variable with functional explanatory variable by using a recursive nonparametric kernel approach. The mean square error and the almost sure convergence of a family of recursive
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77a11d7c65b017b2c53beea1f70c5deb
https://hal.archives-ouvertes.fr/hal-01818838
https://hal.archives-ouvertes.fr/hal-01818838
Publikováno v:
Journal of Multivariate Analysis
Journal of Multivariate Analysis, Elsevier, 2013, 121, pp.50-68. ⟨10.1016/j.jmva.2013.06.004⟩
Journal of Multivariate Analysis, Elsevier, 2013, 121, pp.50-68. ⟨10.1016/j.jmva.2013.06.004⟩
International audience; AMS 2000 subject classifications: 62G08 62G20 62M20 68Q32 62H12 Keywords: Conditional quantile regression Functional covariate Iterative reweighted least squares Reproducing kernel Hilbert space Support vector machine a b s t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a532a7944f55d810876b908945355a9
https://hal.archives-ouvertes.fr/hal-01819413
https://hal.archives-ouvertes.fr/hal-01819413
Autor:
Christophe Crambes, André Mas
Publikováno v:
Bernoulli
Bernoulli, Bernoulli Society for Mathematical Statistics and Probability, 2013, 19 (5B), pp.2627-2651. ⟨10.3150/12-BEJ469⟩
Bernoulli 19, no. 5B (2013), 2627-2651
Bernoulli, Bernoulli Society for Mathematical Statistics and Probability, 2013, 19 (5B), pp.2627-2651. ⟨10.3150/12-BEJ469⟩
Bernoulli 19, no. 5B (2013), 2627-2651
We study prediction in the functional linear model with functional outputs : $Y=SX+\epsilon $ where the covariates $X$ and $Y$ belong to some functional space and $S$ is a linear operator. We provide the asymptotic mean square prediction error with e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b85f595d73a9a5749e868ea1c035d894
http://arxiv.org/abs/0910.3070
http://arxiv.org/abs/0910.3070