A novel correction method for modelling parameter-driven autocorrelated time series with count outcome

A novel correction method for modelling parameter-driven autocorrelated time series with count outcome

Abstract Background Count time series (e.g., daily deaths) are a very common type of data in environmental health research. The series is generally autocorrelated, while the widely used generalized linear model is based on the assumption of independent outcomes. None of the existing methods for mode...

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Bibliographic Details
Main Authors: Xiao-Han Xu, Zi-Shu Zhan, Chen Shi, Ting Xiao, Chun-Quan Ou
Format: Article
Language:English
Published: BMC 2024-03-01
Series:BMC Public Health
Subjects:
Autocorrelated count data
Parameter-driven models
Type I error inflation
Unbiased correction
Interrupted time-series
Online Access:https://doi.org/10.1186/s12889-024-18382-4

Internet

https://doi.org/10.1186/s12889-024-18382-4

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