The combined \(\mathrm{INAR}(p)\) models for time series of counts
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Publication:947183
DOI10.1016/j.spl.2008.01.036zbMath1147.62372OpenAlexW2151554110MaRDI QIDQ947183
Publication date: 29 September 2008
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.spl.2008.01.036
Related Items (20)
On periodic integer-valued moving average (INMA (q)) models ⋮ Integer-Valued Self-Exciting Threshold Autoregressive Processes ⋮ Maximum likelihood estimation of the DDRCINAR(p) model ⋮ Diagnostic checks for integer-valued autoregressive models using expected residuals ⋮ Fluctuations and precise deviations of cumulative INAR time series ⋮ Estimation of parameters in the MDDRCINAR(p) model ⋮ A combined geometric \(INAR(p)\) model based on negative binomial thinning ⋮ Some geometric mixed integer-valued autoregressive (INAR) models ⋮ Integer-valued autoregressive models for counts showing underdispersion ⋮ A mixed INAR(p) model ⋮ Generalized random environment INAR models of higher order ⋮ Integer-valued autoregressive processes with periodic structure ⋮ Random environment integer-valued autoregressive process ⋮ A seasonal geometric INAR process based on negative binomial thinning operator ⋮ A New Class of Autoregressive Models for Time Series of Binomial Counts ⋮ A note on an integer valued time series model with Poisson–negative binomial marginal distribution ⋮ Thinning-based models in the analysis of integer-valued time series: a review ⋮ Estimation in a bivariate integer-valued autoregressive process ⋮ The combined Poisson INMA\((q)\) models for time series of counts ⋮ A geometric time series model with dependent Bernoulli counting series
Cites Work
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- Discrete analogues of self-decomposability and stability
- THE INTEGER-VALUED AUTOREGRESSIVE (INAR(p)) MODEL
- Modelling Count Data Time Series with Markov Processes Based on Binomial Thinning
- An integer-valued pth-order autoregressive structure (INAR(p)) process
- A New Type of Discrete Self-Decomposability and Its Application to Continuous-Time Markov Processes for Modeling Count Data Time Series
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