Useful models for time series of counts or simply wrong ones?
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Publication:1633221
DOI10.1007/s10182-010-0139-9zbMath1443.62269OpenAlexW2045737500MaRDI QIDQ1633221
Robert C. Jung, A. R. Tremayne
Publication date: 19 December 2018
Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10182-010-0139-9
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Uses Software
Cites Work
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- Modelling time series of counts with overdispersion
- Maximum likelihood estimation for an observation driven model for Poisson counts
- Estimation in conditional first order autoregression with discrete support
- A hierarchical Bayes approach to estimation and prediction for time series of counts
- Feasible parameter regions for alternative discrete state space models
- Time series of count data: Modeling, estimation and diagnostics
- Generalized autoregressive conditional heteroscedasticity
- Estimating time series models for count data using efficient importance sampling
- Thinning operations for modeling time series of counts -- a survey
- Observation-driven models for Poisson counts
- Poisson Autoregression
- Markov Regression Models for Time Series: A Quasi-Likelihood Approach
- Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach
- THE INTEGER-VALUED AUTOREGRESSIVE (INAR(p)) MODEL
- A negative binomial model for time series of counts
- Integer-Valued GARCH Process
- Maximum likelihood estimation of higher-order integer-valued autoregressive processes
- Double Exponential Families and Their Use in Generalized Linear Regression
- A regression model for time series of counts
- Likelihood Ratio Test, Wald Test, and Kuhn-Tucker Test in Linear Models with Inequality Constraints on the Regression Parameters
- The monte carlo newton-raphson algorithm
- On autocorrelation in a Poisson regression model
- Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data
- Theory & Methods: Non‐Gaussian Conditional Linear AR(1) Models
- Bayesian analysis of time series Poisson data
- Analysis of low count time series data by poisson autoregression
- Time series models with univariate margins in the convolution-closed infinitely divisible class
- Truncated Poisson Regression for Time Series of Counts
- Monte Carlo EM Estimation for Time Series Models Involving Counts
- Time series count data regression
- Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
- Binomial thinning models for integer time series
- Convolution-closed models for count time series with applications
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity
- Probabilistic Forecasts, Calibration and Sharpness
- Remarks on a Multivariate Transformation
- Predictive Model Assessment for Count Data
- Multivariate statistical modelling based on generalized linear models.
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