相关论文: Seasonal and Periodic Patterns in US COVID-19 Mort…

The outbreak of the SARS-CoV-2 virus, which led to an unprecedented global pandemic, has underscored the critical importance of understanding seasonal patterns. This knowledge is fundamental for decision-making in healthcare and public…

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…

The Variable Bandpass Periodic Block Bootstrap(VBPBB) is an innovative method for time series with periodically correlated(PC) components. This method applies bandpass filters to extract specific PC components from datasets, effectively…

This research introduces a novel approach to resampling periodically correlated (PC) time series using bandpass filters for frequency separation called the Variable Bandpass Periodic Block Bootstrap (VBPBB) and then examines the significant…

This work introduces a novel block bootstrap method for time series with multiple periodically correlated (MPC) components called the Variable Multiple Bandpass Periodic Block Bootstrap (VMBPBB). While past methodological advancements…

Air quality is a critical component of environmental health. Monitoring and analysis of particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5) plays a pivotal role in understanding air quality changes. This study focuses…

Incomplete time-series data compromise statistical inference, particularly when the underlying process exhibits periodic structure (e.g., annual or monthly cycles). Conventional imputation procedures rarely account for such temporal…

Electric generation and consumption are an essential component of contemporary living, influencing diverse facets of our daily routines, convenience, and economic progress. There is a high demand for characterizing the periodic pattern of…

Missing data is a pervasive issue in statistical analyses, affecting the reliability and validity of research across diverse scientific disciplines. Failure to adequately address missing data can lead to biased estimates and consequently…

The survival function of length-of-stay in hospital ward and ICU for COVID-19 patients is studied in this paper. Flexible statistical methods are used to estimate this survival function given relevant covariates such as age, sex, obesity…

Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities and biomarkers also changed. We add to the conversation…

The analysis of complex longitudinal data such as COVID-19 deaths is challenging due to several inherent features: (i) Similarly-shaped profiles with different decay patterns; (ii) Unexplained variation among repeated measurements within…

We introduce a nonparametric bootstrap procedure based on a dynamic factor model to construct pointwise prediction intervals for period life-table death counts. The age distribution of death counts is an example of constrained data, which…

To draw real-world evidence about the comparative effectiveness of multiple time-varying treatments on patient survival, we develop a joint marginal structural survival model and a novel weighting strategy to account for time-varying…

Background: Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus is a significant global challenge. Many individuals who become infected have pre-existing conditions that may…

COVID-19 has led to excess deaths around the world, however it remains unclear how the mortality of other causes of death has changed during the pandemic. Aiming at understanding the wider impact of COVID-19 on other death causes, we study…

This paper introduces new methods to track the offset between two multivariate time series on a continuous basis. We then apply this framework to COVID-19 counts on a state-by-state basis in the United States to determine the progression…

Learning causal graphs from multivariate time series is a ubiquitous challenge in all application domains dealing with time-dependent systems, such as in Earth sciences, biology, or engineering, to name a few. Recent developments for this…

Identifying structural change is a crucial step in analysis of time series and panel data. The longer the time span, the higher the likelihood that the model parameters have changed as a result of major disruptive events, such as the…

We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases one-week ahead of the current time, at the county-level and weekly aggregated, in the United States. A notable…