PERIODOGRAM REGRESSION: A TWO-STAGE MIXED EFFECTS APPROACH FOR MODELLING MULTIPLE INTEGER-VALUED TIME SERIES OF TROPICAL CYCLONE FREQUENCY

• 成果类型: Article
• 署名作者: Zhang, Lyuyuan; Qian, Guoqi; Das, Sourav
• 署名单位: University of Melbourne; James Cook University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1895
• 发表日期: 2025
• 页码: 3-27
• 关键词: enso

摘要

Tropical cyclones (TC) are significant indicators of evolving climate dynamics. Two primary responses of interest are the cyclone frequency and intensity. In this paper we propose a novel integrated modelling framework for simultaneous modelling of TC frequency across several meteorological regions within Australasia. The key methodological insight is to model the second-order properties of multiple integer-valued time series in frequency domain, instead of parametric time domain models. We take a two-stage semiparametric approach, where large scale environmental variation is modelled using generalized linear models while the stochastic variation, including spatial heterogeneity, is estimated using spectral analysis of time series under a hierarchical generating model. Using longitudinal data analysis, we are able to jointly model periodicities in TC frequencies and their correlation with El Ni & ntilde;o-Southern Oscillation (ENSO) cycles as well as the spatial variation between regions. We project the fitted model to obtain one-step-ahead forecasts under the principle of best linear unbiased estimation. This semiparametric approach allows us to obliterate the uniqueness matter of parametric integer-valued time series modelling. Additional methodological advantages include tests for spatial heterogeneity and temporal second-order stationarity. The data analysis corroborates previous findings on declining trend of tropical cyclone frequencies, in the short-term.