Unsupervised data classification using pairwise Markov chains with automatic copulas selection

DOI10.1016/j.csda.2013.01.027zbMath1468.62045OpenAlexW2113799356MaRDI QIDQ1800063

Stéphane Derrode, Wojciech Pieczynski

Publication date: 19 October 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2013.01.027

zbMATH Keywords

copulasiterative conditional estimationpairwise Markov chain

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

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Fast smoothing in switching approximations of non-linear and non-Gaussian models ⋮ Non-stationary data segmentation with hidden evidential semi-Markov chains ⋮ Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data ⋮ Pairwise Markov models and hybrid segmentation approach ⋮ Regenerativity of Viterbi process for pairwise Markov models ⋮ Lower bounds for moments of global scores of pairwise Markov chains ⋮ Exponential forgetting of smoothing distributions for pairwise Markov models ⋮ Existence of infinite Viterbi path for pairwise Markov models

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