Kernel regression with functional response

By Frédéric Ferraty, Ali Laksaci, Amel Tadj, Philippe Vieu. 2011

84 citations recorded by the source corpus.

: We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.

Subfields: Mathematical Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a84a962fe00664d3abc8ca3ddd0ed9aa3e67076a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mathematical Physics

1. Victor M. Panaretos
2. Shahin Tavakoli
3. Philippe Vieu

In Statistics and Probability

1. Nengxiang Ling
2. Victor M. Panaretos
3. Shahin Tavakoli
4. Philippe Vieu
5. Heng Lian
6. Peter Hall
7. Frédéric Ferraty
8. H. Muller

Citations in the corpus

• Nonparametric modelling for functional data: selected survey and tracks for future (2018, background): “The asymptotic behaviour of kernel estimate (3) has been investigated in the situationwhen both covariate and response are infinite dimensional: complete rates of convergence are obtained in [76,77] while asymptotic normality and theoretical validity”
• Fourier analysis of stationary time series in function space (2013, methodology): “In this sense, our methods can be seen as functional smoothing, as overviewed in Ferraty and Vieu (2006), but in an operator context; see also, for example, Ferraty et al. (2011a), Ferraty et al. (2011b), Laib and Louani (2010).”