📝 SummarXiv

Abstract

Seasonal patterns of the incidence, hospital visits, and mortality of ischemic heart disease (IHD) have been widely reported. This study aims to investigate seasonal and periodic patterns of IHD hospitalizations in New York using a novel bootstrap approach, the Variable Bandpass Periodic Block Bootstrap (VBPBB) method. Using a bandpass filter, VBPBB isolates the periodically correlated (PC) component of interest from other PC components and noise before bootstrapping, preserving correlation structures and yielding more precise 95\% confidence intervals than existing periodic bootstrapping methods. We examine weekly, monthly, and annual patterns, along with their harmonic frequencies, in the IHD hospitalization. In addition to the pre-defined frequencies, we also examine the frequencies with the highest amplitudes in the periodogram. By aggregating bootstrap results from statistically significant PC components, a 95\% CI band that preserves multiple periodic correlation structures was obtained. Statistically significant variation was observed for the weekly, annual component, and its 2nd, 3rd, 5th, and 6th harmonics. CI bands obtained from VBPBB were much narrower than those from existing periodic bootstrapping methods. VBPBB substantially improves the precision of periodic mean estimates while preserving periodic correlation structures, making it suitable for time series with multiple periodic patterns and high noise, such as in environmental or healthcare data.