Add to ORKGIn this paper, we discuss concepts and methods of nonlinear regression for functional data. The focus is on the case where covariates and responses are functions. We present a general framework for modelling functional regression problem in the Reproducing Kernel Hilbert Space (RKHS). Basics concepts of kernel regression analysis in the real case are extended to the domain of functional data analysis. Our main results show how using Hilbert spaces theory to estimate a regression function from observed functional data. This procedure can be thought of as a generalization of scalar-valued nonlinear regression estimate
<p>Many scientific studies collect data where the response and predictor variables are both function...
This paper reviews the functional aspects of statistical learning theory. The main point under consi...
In this paper, we study a regression model in which explanatory variables are sampling points of a c...
International audienceThis paper deals with functional regression, in which the input attributes as ...
In this paper, we discuss concepts and methods of nonlinear regression for functional data. The focu...
The aim of this thesis is to systematically investigate some functional regression models for accura...
International audienceIn this paper we consider the problems of supervised classification and regres...
We study in this paper a smoothness regularization method for functional linear regression and provi...
In this paper we present a nonparametric method for extending functional regression methodology to t...
International audienceIn this paper we present a nonparametric method for extending functional regre...
Functional data are difficult to manage for many traditional statistical techniques given their very...
International audienceIn this paper, we develop new estimation results for functional regressions wh...
This paper reviews the functional aspects of statistical learning theory. The main point under con-s...
In this paper, we study a regression model in which explanatory variables are sampling points of a c...
The basic philosophy of Functional Data Analysis (FDA) is to think of the observed data functions as...
<p>Many scientific studies collect data where the response and predictor variables are both function...
This paper reviews the functional aspects of statistical learning theory. The main point under consi...
In this paper, we study a regression model in which explanatory variables are sampling points of a c...
International audienceThis paper deals with functional regression, in which the input attributes as ...
In this paper, we discuss concepts and methods of nonlinear regression for functional data. The focu...
The aim of this thesis is to systematically investigate some functional regression models for accura...
International audienceIn this paper we consider the problems of supervised classification and regres...
We study in this paper a smoothness regularization method for functional linear regression and provi...
In this paper we present a nonparametric method for extending functional regression methodology to t...
International audienceIn this paper we present a nonparametric method for extending functional regre...
Functional data are difficult to manage for many traditional statistical techniques given their very...
International audienceIn this paper, we develop new estimation results for functional regressions wh...
This paper reviews the functional aspects of statistical learning theory. The main point under con-s...
In this paper, we study a regression model in which explanatory variables are sampling points of a c...
The basic philosophy of Functional Data Analysis (FDA) is to think of the observed data functions as...
<p>Many scientific studies collect data where the response and predictor variables are both function...
This paper reviews the functional aspects of statistical learning theory. The main point under consi...
In this paper, we study a regression model in which explanatory variables are sampling points of a c...