Liu, Juxin2024-04-092024-04-0920242024-042024-04-08April 2024https://hdl.handle.net/10388/15566One of the key challenges hindering accurate statistical modeling of infectious COVID-19 data is the dynamic nature of the evolving virus variants. Traditional statistical models typically assume key features of a phenomenon (such as associations between infection and mortality rates of a virus) remain constant, which is not the case with many infectious diseases. Motivated by the need for accurate predictions during the recent COVID-19 pandemic, this thesis considers a novel modeling approach: lagged time-varying coefficient regression. These models are a powerful tool which accounts for dynamic relationships among variables. This thesis builds upon the work of Liu et al. (2023), who demonstrate that lagged time-varying coefficient models yield promising results, especially for COVID-19 time series with complex relationships between reported case and death counts. However, the aforementioned paper does not exhaustively explore the impact of using different methods for bandwidth selection (a key component of time-varying coefficient regression) nor the effectiveness of these models on different types of time series (cumulative vs. daily) and different lengths of time series. For this reason, this thesis investigates several enhancements Liu et al.’s models, including assessing global bandwidth selection procedures, the effectiveness of using different data types and the impact of different time series lengths. An R implementation for these models is provided.application/pdfenRegressionLocal Polynomial RegressionTime-Varying CoefficientsKernel FunctionsBandwidthsStatistical ModelingPredictionCOVID-19Thesis2024-04-09