On the using of modal curves for radar waveforms classification

DOI10.1016/j.csda.2006.07.012zbMath1162.62378OpenAlexW1963480946MaRDI QIDQ1020147

Frédéric Ferraty, Philippe Vieu, Sophie Dabo-Niang

Publication date: 29 May 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.07.012

zbMATH Keywords

cluster analysisnonparametric statisticssatellite datahierarchical methodfunctional statisticscurves classificationcurves datamodal curveradar waveforms

Mathematics Subject Classification ID

Density estimation (62G07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05) Applications of statistics in engineering and industry; control charts (62P30)

Related Items

Defining probability density for a distribution of random functions, Classification methods for Hilbert data based on surrogate density, A partial overview of the theory of statistics with functional data, Multi-spectral decomposition of functional autoregressive models, On the Validity of the Bootstrap in Non-Parametric Functional Regression, Using mathematical morphology for unsupervised classification of functional data, Unsupervised learning of regression mixture models with unknown number of components, Statistics for functional data, Cross-validated estimations in the single-functional index model, Quantifying the closeness to a set of random curves via the mean marginal likelihood

Uses Software

• R
• fda (R)
• sm

Cites Work

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• On the kernel rule for function classification
• Curves discrimination: a nonparametric functional approach
• Impartial trimmed \(k\)-means for functional data
• The dip test of unimodality
• Functional data analysis
• Median balls: An extension of the interquantile intervals to multivariate distributions
• Linear processes in function spaces. Theory and applications
• Applied functional data analysis. Methods and case studies
• Clustering functional data
• Nonparametric functional data analysis. Theory and practice.
• Nonparametric Estimation of the Mode of A Distribution of Random Curves
• Unsupervised Curve Clustering using B‐Splines
• Simple estimation of the mode of a multivariate density
• Estimating the number of clusters
• Convergent estimators for the l₁-median of banach valued random variable
• Cluster analysis: a further approach based on density estimation.
• Parametric modelling of growth curve data: An overview. (With comments)