Markov regression models for count time series with excess zeros: a partial likelihood approach

DOI10.1016/j.stamet.2013.02.001zbMath1486.62248OpenAlexW2047315330MaRDI QIDQ1756183

Xianqiang Yang

Publication date: 14 January 2019

Published in: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.stamet.2013.02.001

zbMATH Keywords

EM algorithmPoisson distributionautoregressivezero-inflation

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (5)

State-space models for count time series with excess zeros ⋮ Autoregressive and moving average models for zero‐inflated count time series ⋮ Analysis of over-dispersed count data with extra zeros using the Poisson log-skew-normal distribution ⋮ Time series regression for zero-inflated and overdispersed count data: a functional response model approach ⋮ Copula-based Markov zero-inflated count time series models with application

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