A BINAR(1) time-series model with cross-correlated COM–Poisson innovations
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Publication:4639106
DOI10.1080/03610926.2017.1316400zbMath1402.62201OpenAlexW2605573019MaRDI QIDQ4639106
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Publication date: 2 May 2018
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2017.1316400
Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics in engineering and industry; control charts (62P30)
Related Items (13)
Comparison of BINAR(1) models with bivariate negative binomial innovations and explanatory variables ⋮ BINMA(1) model with COM-Poisson innovations: Estimation and application ⋮ BINAR(1) negative binomial model for bivariate non-stationary time series with different over-dispersion indices ⋮ A flexible bivariate distribution for count data expressing data dispersion ⋮ A non‐stationary bivariate INAR(1) process with a simple cross‐dependence: Estimation with some properties ⋮ On the theory of periodic multivariate INAR processes ⋮ A BINAR(1) time-series model with cross-correlated COM–Poisson innovations ⋮ Generalized random environment INAR models of higher order ⋮ Modeling longitudinal INMA(1) with COM-Poisson innovation under non-stationarity: application to medical data ⋮ Investigating GQL-based inferential approaches for non-stationary BINAR(1) model under different quantum of over-dispersion with application ⋮ Inference for bivariate integer-valued moving average models based on binomial thinning operation ⋮ A review of INMA integer-valued model class, application and further development ⋮ First-order random coefficient INAR process with dependent counting series
Cites Work
- A geometric bivariate time series with different marginal parameters
- Remarks on asymptotic efficient estimation for regression effects in stationary and nonstationary models for panel count data
- A bivariate \(INAR(1)\) time series model with geometric marginals
- Bivariate Conway-Maxwell-Poisson distribution: formulation, properties, and inference
- A non-stationary integer-valued autoregressive model
- Discrete analogues of self-decomposability and stability
- Regression models for discrete longitudinal responses. With comments and a rejoinder by the authors
- Thinning operations for modeling time series of counts -- a survey
- Inferential methods for an unconstrained nonstationary BINMA time series process with Poisson innovations
- The timing of bid placement and extent of multiple bidding: an empirical investigation using ebay online auctions
- Stationary solutions for integer-valued autoregressive processes
- Bivariate binomial autoregressive models
- Random environment integer-valued autoregressive process
- A robust algorithm for estimating regression and dispersion parameters in non-stationary longitudinally correlated Com–Poisson data
- Estimation in a bivariate integer-valued autoregressive process
- On composite likelihood estimation of a multivariate INAR(1) model
- Estimating Equations for Parameters in Means and Covariances of Multivariate Discrete and Continuous Responses
- Bivariate Time Series Modeling of Financial Count Data
- Autoregressive moving-average processes with negative-binomial and geometric marginal distributions
- Integer-valued moving average (INMA) process
- Some ARMA models for dependent sequences of poisson counts
- Miscellanea. On the efficiency of regression estimators in generalised linear models for longitudinal data
- The Multivariate Ginar(p) Process
- Analysing longitudinal count data with overdispersion
- A Model of the Joint Distribution of Purchase Quantity and Timing
- Estimating the parameters of a BINMA Poisson model for a non-stationary bivariate time series
- A BINAR(1) time-series model with cross-correlated COM–Poisson innovations
- Difference Equations for the Higher‐Order Moments and Cumulants of the INAR(1) Model
- Comparing Joint GQL Estimation and GMM Adaptive Estimation in COM-Poisson Longitudinal Regression Model
- Flexible Bivariate INAR(1) Processes Using Copulas
- A bivariate INAR(1) process with application
- Improved estimation for Poisson INAR(1) models
- The COM‐Poisson model for count data: a survey of methods and applications
- A Useful Distribution for Fitting Discrete Data: Revival of the Conway–Maxwell–Poisson Distribution
- Conjugate analysis of the Conway-Maxwell-Poisson distribution
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