Consistency of a nonparametric estimate of a density function for dependent variables
Journal of Multivariate Analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics)
Editorial: Statistics for Functional Data
Computational Statistics & Data Analysis
PLS classification of functional data
Computational Statistics
Robust estimation and classification for functional data via projection-based depth notions
Computational Statistics
An extension of Fisher's discriminant analysis for stochastic processes
Journal of Multivariate Analysis
Classification of functional data: A segmentation approach
Computational Statistics & Data Analysis
On depth measures and dual statistics. A methodology for dealing with general data
Journal of Multivariate Analysis
Editorial: High-dimensional data: a fascinating statistical challenge
Journal of Multivariate Analysis
Support vector machine for functional data classification
Neurocomputing
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In this paper, we define a n-consistent nonparametric estimator for the marginal density function of an order one stationary process built up from a sample of i.i.d continuous time trajectories. Under mild conditions we obtain strong consistency, strong orders of convergence and derive the asymptotic distribution of the estimator. We extend some of the results to the non-stationary case. We propose a nonparametric classification rule based on local times (occupation measure) and include some simulations studies.