Testing serial dependence or cross dependence for time series with underreporting

• 成果类型: Article
• 署名作者: Wei, Keyao; Wang, Lengyang; Xia, Yingcun
• 署名单位: National University of Singapore; National Centre for Infectious Diseases Singapore
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asae027
• 发表日期: 2024
• 页码: 12931312
• 关键词: ambient-temperature; SURVEILLANCE; bootstrap; systems; disease

摘要

In practice, it is common for collected data to be underreported, an issue that is particularly prevalent in fields such as the social sciences, ecology and epidemiology. Drawing inferences from such data using conventional statistical methods can lead to incorrect conclusions. In this paper, we study tests for serial or cross dependence in time series data that are subject to underreporting. We introduce new test statistics, develop corresponding group-of-blocks bootstrap techniques and establish their consistency. The methods are shown via simulation studies to be efficient and are used to identify key factors responsible for the spread of dengue fever and the occurrence of cardiovascular disease.