BETA REGRESSION FOR TIME SERIES ANALYSIS OF BOUNDED DATA, WITH APPLICATION TO CANADA GOOGLE® FLU TRENDS
成果类型：
Article
署名作者：
Guolo, Annamaria; Varin, Cristiano
署名单位：
University of Verona; Universita Ca Foscari Venezia
刊物名称：
ANNALS OF APPLIED STATISTICS
ISSN/ISSBN：
1932-6157
DOI：
10.1214/13-AOAS684
发表日期：
2014
页码：
74-88
关键词：
摘要：
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 (R) Flu Trends project.
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