Convolution-closed models for count time series with applications

DOI10.1111/j.1467-9892.2010.00697.xzbMath1290.62078OpenAlexW1552769223MaRDI QIDQ4979107

A. R. Tremayne, Robert C. Jung

Publication date: 16 June 2014

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.2010.00697.x

zbMATH Keywords

maximum likelihood estimationcount dataserial dependencehigher-order modelsgeneralized Poissonparametric resampling

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Point estimation (62F10) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10)

Related Items (12)

Useful models for time series of counts or simply wrong ones? ⋮ Bayesian nonparametric forecasting for INAR models ⋮ A flexible INAR(1) time series model with dependent zero-inflated count series and medical contagious cases ⋮ Forecasting overdispersed INAR(1) count time series with negative binomial marginal ⋮ Alternative procedures in dependent counting INAR process with application on COVID-19 ⋮ A threshold mixed count time series model: estimation and application ⋮ A time series model based on dependent zero inflated counting series ⋮ Changepoints in times series of counts ⋮ A Poisson INAR(1) model with serially dependent innovations ⋮ Bootstrapping INAR models ⋮ Thinning-based models in the analysis of integer-valued time series: a review ⋮ Inference for bivariate integer-valued moving average models based on binomial thinning operation

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