A Bayesian nonparametric approach for time series clustering

DOI10.1214/13-BA852zbMath1327.62473MaRDI QIDQ899011

Alberto Contreras-Cristán, Luis E. Nieto-Barajas

Publication date: 21 December 2015

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ba/1393251774

zbMATH Keywords

model-based clusteringtime series analysisdynamic linear modelPitman-Yor processBayes nonparametrics

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to actuarial sciences and financial mathematics (62P05) Nonparametric tolerance and confidence regions (62G15)

Related Items (10)

Clustering Chlorophyll-a satellite data using quantiles ⋮ Adjusting background noise in cluster analyses of longitudinal data ⋮ A scalable Bayesian nonparametric model for large spatio-temporal data ⋮ On a nonparametric change point detection model in Markovian regimes ⋮ Dynamic model-based clustering for spatio-temporal data ⋮ A Bayesian semiparametric vector multiplicative error model ⋮ Model-based approach for household clustering with mixed scale variables ⋮ Bayesian Nonparametric Biostatistics ⋮ A new Dirichlet process for mining dynamic patterns in functional data ⋮ Bayesian spectral analysis models for quantile regression with Dirichlet process mixtures

Uses Software

• BNPdensity

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