Fitting Poisson time-series models using bivariate mixture transition distributions

DOI10.1080/15598608.2013.790241zbMath1423.62113OpenAlexW2070637179MaRDI QIDQ2320864

Yanyan Li

Publication date: 27 August 2019

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/15598608.2013.790241

zbMATH Keywords

EM algorithminternet trafficcontinuous-discrete bivariate distribution modelsPoisson time-series regression models

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics in engineering and industry; control charts (62P30) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (2)

Autoregressive density modeling with the Gaussian process mixture transition distribution ⋮ JOINT MODELING OF CORRELATED TIME DURATIONS AND THEIR MARKS USING A WEIBULL POISSON MARKED POINT PROCESS MIXTURE MODELS

Cites Work

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• Modeling Marked Point Processes via Bivariate Mixture Transition Distribution Models

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