Markov Chain Approach to Forecast in the Binomial Autoregressive Models

Chain binomial models and binomial autoregressive processes.

We establish a connection between a class of chain-binomial models of use in ecology and epidemiology and binomial autoregressive (AR) processes. New results are obtained for the latter, including expressions for the lag-conditional distribution and related quantities. We focus on two types of chain-binomial model, extinction-colonization and colonization-extinction models, and present two appr...

Bivariate binomial autoregressive models

This paper introduces new classes of bivariate time series models being useful to fit count data time series with a finite range of counts. Motivation comes mainly from the comparison of schemes for monitoring tourism demand, stock data, production and environmental processes. All models are based on the bivariate binomial distribution of Type II. First, a new family of bivariate integer-valued...

Development of Markov Chain Grey Regression Model to Forecast the Annual Natural Gas Consumption

Accurate forecasting of annual gas consumption of the country plays an important role in energy supply strategies and policy making in this area.  Markov chain grey regression model is considered to be a superior model for analyzing and forecasting annual gas consumption.  This model Markov is a combination of the Markov chain and grey regression models. According to this model, the residual er...

Web - based Supplementary Materials for “ Chain Binomial Models and Binomial Autoregressive Processes ”

Monte Carlo Markov Chain Exact Inference for Binomial Regression Models

Current methods for conducting exact inference for logistic regression are not capable of handling large data sets due to memory constraints caused by storing large networks. We provide and implement an algorithm which is capable of conducting (approximate) exact inference for large data sets. Various application fields, such as genetic epidemiology, in which logistic regression models are fit ...