Estimating Some Characteristics of the Conditional Distribution in Nonparametric Functional Models
By Frédéric Ferraty, Ali Laksaci, Philippe Vieu. 2006
143 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/f03b080ecb3a34af0bf5d44beacb835601c36d8f
How researchers see this paper
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In Analytical Chemistry
- M'hamed Ezzahrioui
- Elias Ould-Saïd
- Nengxiang Ling
- Philippe Vieu
- André Mas
- Heng Lian
- Peter Hall
- H. Muller
In Statistics and Probability
- Philippe Vieu
- Nengxiang Ling
- Heng Lian
- Frédéric Ferraty
- Elias Ould-Saïd
- M'hamed Ezzahrioui
- Ali Laksaci
- Jacques Demongeot
Citations in the corpus
- Functional data: local linear estimation of the conditional density and its application (2013, background): “Notice that in nonparametric functional statistics, the first results about almost-complete consistency were obtained in 8 , for the kernel-type estimator of the conditional density/distribution functions when the data are independent and identically distributed.”
- NONPARAMETRIC REGRESSION ON FUNCTIONAL DATA: INFERENCE AND PRACTICAL ASPECTS (2007, background): “They also briefly discuss the nongaussian case (see Mayer-Wolf & Zeitouni, 1993, Remark 3, p. 19), but many other authors have consider different settings (see Ferraty et al., 2006, for lengthy discussion and references therein).”
- Asymptotic normality of a nonparametric estimator of the conditional mode function for functional data (2008, background): “[18] regarding the uniform strong consistency with rates.”
- On the Local Linear Modelization of the Conditional Distribution for Functional Data (2014, result): “Remember that this hypothesis is similar to the assumption (H2) in Ferraty et al. (2006).”
- Nonparametric modelling for functional data: selected survey and tracks for future (2018, methodology): “[93], and it has been used in most of the literature discussed in (ii) and (iii) above.”
- Asymptotic Results of a Nonparametric Conditional Quantile Estimator for Functional Time Series (2008, methodology): “Here, we used the norm defined by the L2 distance between the second derivatives of the curves (for further discussion see Ferraty et al., 2006).”