SKEWED AUTO-REGRESSIVE PROCESS WITH EXOGENOUS INPUT VARIABLES: AN APPLICATION IN THE ADMINISTERED VACCINE DOSES ON COVID-19 SPREAD

DOI10.1142/S0218348X2240148XOpenAlexW4210444834MaRDI QIDQ5101539

Mohammad Reza Mahmoudi, Hamid Bidram, Amir Mosavi, Mohsen Maleki

Publication date: 30 August 2022

Published in: Fractals (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0218348x2240148x

zbMATH Keywords

coronavirustime seriesCOVID-19auto-regressive with exogenous inputsCOVID-19 vaccinetwo-piece scale mixtures

Mathematics Subject Classification ID

Statistics (62-XX) Inference from stochastic processes (62Mxx) Probabilistic methods, stochastic differential equations (65Cxx)

Uses Software

• R

Cites Work

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• Heteroscedastic nonlinear regression models using asymmetric and heavy tailed two-piece distributions
• Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models
• Fractional unit-root tests allowing for a fractional frequency flexible Fourier form trend: predictability of Covid-19
• Caputo SIR model for COVID-19 under optimized fractional order
• The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence
• Nonlinear semiparametric autoregressive model with finite mixtures of scale mixtures of skew normal innovations
• A Bayesian approach on the two-piece scale mixtures of normal homoscedastic nonlinear regression models

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