This article develops nonlinear functional forms for modeling count time series of daily deaths due to the COVID-19 virus. Our models explain mean levels while accounting time-varying variances. A Bayesian approach using Markov chain Monte Carlo (MCMC) is adopted analysis, inference and forecasting under proposed models. Applications are shown death counts from several countries affected by pan...

Statistical inference for discrete-valued time series has not been developed like traditional methods generated by continuous random variables. Some relevant models exist, but the lack of a homogenous framework raises some critical issues. For instance, it is trivial to explore whether are nested and quite arduous derive stochastic properties which simultaneously hold across different specifica...