Add to ORKGIn this paper, we propose kernel-based smooth estimates of the functional principal components when data are continuous trajectories of stochastic processes. Strong consistency and the asymptotic distribution are derived under mild conditions.Functional principal components Kernel methods Hilbert-Schmidt operators Eigenfunctions
AbstractPrincipal component analysis (PCA) is one of the key techniques in functional data analysis....
We introduce a simple and interpretable model for functional data analysis for situations where the ...
The aim of this dissertation is to create a unified and practical approach to the analysis of correl...
We study the properties of the eigenvalues of Gram matrices in a non-asymptotic setting. Using local...
We study the properties of the eigenvalues of Gram matrices in a non-asymptotic setting. Using local...
International audienceWe study the properties of the eigenvalues of Gram matrices in a non-asymptoti...
In this paper, we study a regression model in which explanatory variables are sampling points of a c...
We study in this paper a smoothness regularization method for functional linear regression and provi...
Functional data objects are usually collected sequentially over time exhibiting forms of dependence....
We consider functional data analysis when the observations at each location are functional rather th...
We introduce a simple and interpretable model for functional data analysis for situations where the ...
The spectral theory for weakly stationary processes valued in a separable Hilbert space has known re...
The spectral theory for weakly stationary processes valued in a separable Hilbert space has known re...
Functional principal component analysis (FPCA) based on the Karhunen-Loève decomposition has been su...
We consider the estimation of the operator of one-order functional autoregressive process by the sie...
AbstractPrincipal component analysis (PCA) is one of the key techniques in functional data analysis....
We introduce a simple and interpretable model for functional data analysis for situations where the ...
The aim of this dissertation is to create a unified and practical approach to the analysis of correl...
We study the properties of the eigenvalues of Gram matrices in a non-asymptotic setting. Using local...
We study the properties of the eigenvalues of Gram matrices in a non-asymptotic setting. Using local...
International audienceWe study the properties of the eigenvalues of Gram matrices in a non-asymptoti...
In this paper, we study a regression model in which explanatory variables are sampling points of a c...
We study in this paper a smoothness regularization method for functional linear regression and provi...
Functional data objects are usually collected sequentially over time exhibiting forms of dependence....
We consider functional data analysis when the observations at each location are functional rather th...
We introduce a simple and interpretable model for functional data analysis for situations where the ...
The spectral theory for weakly stationary processes valued in a separable Hilbert space has known re...
The spectral theory for weakly stationary processes valued in a separable Hilbert space has known re...
Functional principal component analysis (FPCA) based on the Karhunen-Loève decomposition has been su...
We consider the estimation of the operator of one-order functional autoregressive process by the sie...
AbstractPrincipal component analysis (PCA) is one of the key techniques in functional data analysis....
We introduce a simple and interpretable model for functional data analysis for situations where the ...
The aim of this dissertation is to create a unified and practical approach to the analysis of correl...