Estimation in an Integer-Valued Autoregressive Process with Negative Binomial Marginals (NBINAR(1))

DOI10.1080/03610926.2010.529528zbMath1237.62125OpenAlexW1998237498MaRDI QIDQ2884863

Hassan S. Bakouch, Aleksandar S. Nastić, Miroslav M. Ristić

Publication date: 18 May 2012

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2010.529528

zbMATH Keywords

negative binomial distributionstationaritynegative binomial thinningburglary data

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Point estimation (62F10) Inference from stochastic processes and spectral analysis (62M15)

Related Items (23)

Poisson-Lindley INAR(1) model with applications ⋮ Zero-Inflated NGINAR(1) process ⋮ Estimation and testing for the integer-valued threshold autoregressive models based on negative binomial thinning ⋮ An INAR(1) model based on the Pegram and thinning operators with serially dependent innovation ⋮ A study of RCINAR(1) process with generalized negative binomial marginals ⋮ A new INAR model based on Poisson-BE2 innovations ⋮ Forecasting overdispersed INAR(1) count time series with negative binomial marginal ⋮ Alternative procedures in dependent counting INAR process with application on COVID-19 ⋮ Some geometric mixed integer-valued autoregressive (INAR) models ⋮ A class of observation-driven random coefficient INAR(1) processes based on negative binomial thinning ⋮ On Shifted Geometric INAR(1) Models Based on Geometric Counting Series ⋮ Noise-indicator nonnegative integer-valued autoregressive time series of the first order ⋮ An integer-valued threshold autoregressive process based on negative binomial thinning ⋮ An INAR(1) model based on a mixed dependent and independent counting series ⋮ A new class of INAR(1) model for count time series ⋮ 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 ⋮ The combined Poisson INMA\((q)\) models for time series of counts ⋮ Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued autoregressive processes ⋮ Mixed Poisson INAR(1) processes ⋮ A geometric time series model with dependent Bernoulli counting series ⋮ Modelling and monitoring of INAR(1) process with geometrically inflated Poisson innovations ⋮ A mixed thinning based geometric INAR(1) model

Cites Work

• Unnamed Item
• Zero truncated Poisson integer-valued AR\((1)\) model
• Some autoregressive moving average processes with generalized Poisson marginal distributions
• Estimation in conditional first order autoregression with discrete support
• Negative binomial time series models based on expectation thinning operators
• A new geometric first-order integer-valued autoregressive (NGINAR(1)) process
• Time series: theory and methods
• Discrete analogues of self-decomposability and stability
• First-order random coefficient integer-valued autoregressive processes
• Asymptotic properties of CLS estimators in the Poisson AR(1) model
• Asymptotic distribution of the Yule--Walker estimator for INAR\((p)\) processes
• Stationary solutions for integer-valued autoregressive processes
• THE INTEGER-VALUED AUTOREGRESSIVE (INAR(p)) MODEL
• Inference for pth-order random coefficient integer-valued autoregressive processes
• Autoregressive moving-average processes with negative-binomial and geometric marginal distributions
• First-Order Integer-Valued Autoregressive (INAR (1)) Process: Distributional and Regression Properties
• First order autoregressive time series with negative binomial and geometric marginals
• Existence and Stochastic Structure of a Non-negative Integer-valued Autoregressive Process
• Explicit stationary distributions for some galton-watson processes with immigration
• FIRST-ORDER INTEGER-VALUED AUTOREGRESSIVE (INAR(1)) PROCESS
• A New Type of Discrete Self-Decomposability and Its Application to Continuous-Time Markov Processes for Modeling Count Data Time Series

This page was built for publication: Estimation in an Integer-Valued Autoregressive Process with Negative Binomial Marginals (NBINAR(1))