Testing serial dependence or cross dependence for time series with underreporting
针对社会科学、生态学和流行病学中常见的漏报数据，提出了检验时间序列序列依赖或交叉依赖的新统计量和分组块自助法，并通过模拟和实际数据验证了有效性。
Abstract 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.