Model selection for time series of count data

DOI10.1016/j.csda.2018.01.002zbMath1469.62013OpenAlexW2781767908MaRDI QIDQ1662312

Naif Alzahrani, Simon E. F. Spencer, Trevelyan J. McKinley, Panayiota Touloupou, Peter Neal

Publication date: 17 August 2018

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

Full work available at URL: http://wrap.warwick.ac.uk/97167/7/WRAP-model-selection-time-series-count-data-Spencer-2018.pdf

zbMATH Keywords

marginal likelihoodparticle filterMCMCINGARCH modelautoregressive Poisson regression modelINAR model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15)

Related Items (8)

Consistent model selection procedure for general integer-valued time series ⋮ Variable selection for an improved INAR(1) model with explanatory variables using 2SPCLS ⋮ Efficient Bayesian model choice for partially observed processes: with application to an experimental transmission study of an infectious disease ⋮ Identification of the relative timing of infectiousness and symptom onset for outbreak control ⋮ Bayesian model discrimination for partially-observed epidemic models ⋮ Random coefficients integer-valued threshold autoregressive processes driven by logistic regression ⋮ Necessary and sufficient conditions for the identifiability of observation‐driven models ⋮ Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued autoregressive processes

Uses Software

• tsbridge

Cites Work

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