@article {10.3844/jmssp.2021.50.58,
article_type = {journal},
title = {Robust Modelling of Coronavirus Cases in Egypt: Poisson INARCH and Negative Binomial INARCH},
author = {Elsaied, Hanan},
volume = {17},
year = {2021},
month = {Jun},
pages = {50-58},
doi = {10.3844/jmssp.2021.50.58},
url = {https://thescipub.com/abstract/jmssp.2021.50.58},
abstract = {This study compares robust Poisson INARCH(P) models (more briefly: RP-INARCH) and robust negative binomial INARCH(p) models (more briefly: RNB-INARCH) to fit the new daily confirmed cases for the first wave of COVID 19 in Egypt. The robust estimation of these models is based on some modifications of the Conditional Maximum Likelihood Estimates (CMLE). The simulation results show that RNB-INARCH is more robust than RP-INARCH, but less efficient if the data contain isolated or patched additive outliers in terms of the bias calculation, whereby the low-bias model is more robust. These results are confirmed by the application study on COVID-19 data. The Akaike Information Criterion (AIC) is also compared for these models.},
journal = {Journal of Mathematics and Statistics},
publisher = {Science Publications}
}