Bounded time series consisting of rates or proportions are often encountered in applications. This manuscript proposes a practical approach to analyze bounded time series, through a beta regression model. The method allows the direct interpretation of the regression parameters on the original response scale, while properly accounting for the heteroskedasticity typical of bounded variables. The serial dependence is modeled by a Gaussian copula, with a correlation matrix corresponding to a stationary autoregressive and moving average process. It is shown that inference, prediction, and control can be carried out straightforwardly, with minor modifications to standard analysis of autoregressive and moving average models. The methodology is motivated by an application to the influenza-like-illness incidence estimated by the Google® Flu Trends project.
|Data di pubblicazione:||2014|
|Titolo:||Beta regression for time series analysis of bounded data, with application to Canada Google Flu Trends.|
|Rivista:||THE ANNALS OF APPLIED STATISTICS|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1214/13-AOAS684|
|Appare nelle tipologie:||2.1 Articolo su rivista |
File in questo prodotto:
|AOAS684.pdf||Versione dell'editore||Accesso chiuso-personale||Riservato|