Bayesian nonparametric forecasting for INAR models

DOI10.1016/j.csda.2014.12.011zbMath1466.62034OpenAlexW1992421792MaRDI QIDQ1659101

Antonio Canale, Luisa Bisaglia

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2318/1508013

zbMATH Keywords

forecastingGibbs sampling algorithmDirichlet process mixturescount time seriesINAR(1)

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (4)

Zero‐Modified Geometric INAR(1) Process for Modelling Count Time Series with Deflation or Inflation of Zeros ⋮ On MCMC sampling in self-exciting integer-valued threshold time series models ⋮ Mixed Poisson INAR(1) processes ⋮ Integer-valued autoregressive processes with prespecified marginal and innovation distributions: a novel perspective

Cites Work

• On a class of Bayesian nonparametric estimates: I. Density estimates
• Estimation in conditional first order autoregression with discrete support
• Discrete analogues of self-decomposability and stability
• Compound Poisson INAR(1) processes: stochastic properties and testing for overdispersion
• Prior distributions on spaces of probability measures
• A Bayesian analysis of some nonparametric problems
• Efficient Estimation of Auto-Regression Parameters and Innovation Distributions for Semiparametric Integer-Valued AR(p) Models
• Bayesian Kernel Mixtures for Counts
• Mixed Poisson Distributions
• Some ARMA models for dependent sequences of poisson counts
• Semiparametric regression for count data
• Efficient Probabilistic Forecasts for Counts
• Analysis of low count time series data by poisson autoregression
• FIRST-ORDER INTEGER-VALUED AUTOREGRESSIVE (INAR(1)) PROCESS
• Bayesian Density Estimation and Inference Using Mixtures
• Convolution-closed models for count time series with applications
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Unnamed Item

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