On the Use of Reproducing Kernel Hilbert Spaces in Functional Classification
DOI10.1080/01621459.2017.1320287zbMath1402.68152arXiv1507.04398OpenAlexW2485393623MaRDI QIDQ4559702
José L. Torrecilla, José R. Berrendero, Antonio Cuevas
Publication date: 4 December 2018
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1507.04398
variable selectionRadon-Nikodym derivativesabsolutely continuitysupervised functional classificationmutually singular processes
Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22)
Related Items
Cites Work
- Unnamed Item
- Unnamed Item
- Supervised classification of diffusion paths
- A partial overview of the theory of statistics with functional data
- Inference for functional data with applications
- Methodology and theory for partial least squares applied to functional data
- Variable selection in infinite-dimensional problems
- Equivalence and perpendicularity of Gaussian processes
- Radon-Nikodym derivatives with respect to measures induced by discontinuous independent-increment processes
- Classification methods for Hilbert data based on surrogate density
- Nonparametric functional data analysis. Theory and practice.
- On equivalence of Gaussian measures
- Variable selection in functional data classification: a maxima-hunting proposal
- Componentwise classification and clustering of functional data
- Supervised Classification for a Family of Gaussian Functional Models
- Logistic Regression With Brownian-Like Predictors
- An Approach to Time Series Analysis
- Modern Multivariate Statistical Techniques
- The mRMR variable selection method: a comparative study for functional data
- Radon-Nikodym Derivatives of Gaussian Measures
- Extraction and Detection Problems and Reproducing Kernel Hilbert Spaces
- On Gaussian Measures Equivalent to Wiener Measure
- Achieving near Perfect Classification for Functional Data
- Brownian Motion