An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …

The COVID‐19 pandemic has highlighted the importance of reliable statistical models
which, based on the available data, can provide accurate forecasts and impact analysis of …

Recent outbreak of the novel corona virus COVID-19 has affected all of our lives in one way
or the other. While medical researchers are working hard to find a cure and doctors/nurses …

The past few decades have seen considerable interest in modeling time series of counts,
with applications in many domains. Classical and Bayesian modeling have primarily …

In the hospitality industry, lead time data are a form of compositional data that are crucial for
business planning, resource allocation, and staffing. Hospitality businesses accrue fees …

Count-valued time series data are routinely collected in many application areas. We are
particularly motivated to study the count time series of daily new cases, arising from the …

When dealing with time series with outlying and atypical data, a commonly used approach is
to develop models based on heavy-tailed distributions. The literature coping with continuous …

Estimation in GARMA models has traditionally been carried out under the frequentist
approach. To date, Bayesian approaches for such estimation have been relatively limited. In …

A new forecasting method based on the concept of the profile predictive likelihood function
is proposed for discrete‐valued processes. In particular, generalized autoregressive moving …

This work presents a Bayesian approach for the estimation of Beta Autoregressive Moving
Average ($\beta $ ARMA) models. We discuss standard choice for the prior distributions and …