On stochastic dynamic modeling of incidence data

Abstract

In this paper, a Markov Regime Switching Model of Conditional Mean with covariates, is proposed and investigated for the analysis of incidence rate data. The components of the model are selected by both penalized likelihood techniques in conjunction with the Expectation Maximization algorithm, with the goal of achieving a high level of robustness regarding the modeling of dynamic behaviors of epidemiological data. In addition to statistical inference, Changepoint Detection Analysis is performed for the selection of the number of regimes, which reduces the complexity associated with Likelihood Ratio Tests. Within this framework, a three-phase procedure for modeling incidence data is proposed and tested via real and simulated data.

Forecasting the Power Generation Mix in Italy Based on Grey Markov Models

“…It should be noted that in the cases of Biofuel and Solar, it was not possible to calculate the representative values for all states because some states were never reached, as the relative errors pertained only to certain states. To overcome this problem, we employed Change Point analysis to identify the optimal point at which to truncate each data series [33]. The truncation point was determined to be in the year 2010 for Biofuel and in 2011 for Solar.…”

Forecasting the Power Generation Mix in Italy Based on Grey Markov Models

“…It should be noted that in the cases of Biofuel and Solar, it was not possible to calculate the representative values for all states because some states were never reached, as the relative errors pertained only to certain states. To overcome this problem, we employed Change Point analysis to identify the optimal point at which to truncate each data series [31]. The truncation point was determined to be in the year 2010 for Biofuel and in 2011 for Solar.…”