Bayesian time‐varying autoregressive models of COVID‐19 epidemics
From MaRDI portal
Publication:6149268
DOI10.1002/bimj.202200054OpenAlexW4287307734MaRDI QIDQ6149268
Arkaprava Roy, Paolo Giudici, Unnamed Author
Publication date: 4 March 2024
Published in: Biometrical Journal (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/bimj.202200054
Cites Work
- Unnamed Item
- Unnamed Item
- Modeling overdispersed or underdispersed count data with generalized Poisson integer-valued GARCH models
- Zero-inflated Poisson and negative binomial integer-valued GARCH models
- Modeling time series of counts with COM-Poisson INGARCH models
- Time-varying auto-regressive models for count time-series
- Normalized least-squares estimation in time-varying ARCH models
- Statistical inference for time-varying ARCH processes
- Observation-driven models for Poisson counts
- Integer-Valued GARCH Process
- Extreme Value Theory for GARCH Processes
- A regression model for time series of counts
- Optimal Scaling of Discrete Approximations to Langevin Diffusions
- Functional-Coefficient Regression Models for Nonlinear Time Series
- Functional Coefficient Regression Models for Non-linear Time Series: A Polynomial Spline Approach
- Monte Carlo EM Estimation for Time Series Models Involving Counts
- Estimation and prediction of time-varying GARCH models through a state-space representation: a computational approach
- Nonparametric estimation of a time-varying GARCH model
- Strictly Proper Scoring Rules, Prediction, and Estimation
- The elements of statistical learning. Data mining, inference, and prediction
This page was built for publication: Bayesian time‐varying autoregressive models of COVID‐19 epidemics