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Full-Text Articles in Physical Sciences and Mathematics
Methods For Making Policy-Relevant Forecasts Of Infectious Disease Incidence, Stephen A. Lauer
Methods For Making Policy-Relevant Forecasts Of Infectious Disease Incidence, Stephen A. Lauer
Doctoral Dissertations
Infectious diseases place an enormous burden on the people of the developing world and their governments. When, where, and how to allocate resources in order to slow the spread of a virus or deal with the aftermath of an outbreak is often the responsibility of local public health officials. In this thesis, we develop statistical methods for forecasting future incidence of infectious diseases and estimating the effects of interventions designed to reduce future incidence, bearing in mind the needs and concerns of those public health officials. While most infectious disease forecasting models focus on short-term horizons (i.e. weeks or …
Exploring A Bayesian Analysis Of Opinion Dynamics Using The Approximate Bayesian Computation Method, Jessica L. Bishop
Exploring A Bayesian Analysis Of Opinion Dynamics Using The Approximate Bayesian Computation Method, Jessica L. Bishop
Graduate Research Theses & Dissertations
Social media has created a whole new framework in the way we understand ones expression of opinion, and how ones' opinion can influence others. Models of opinion dynamics, such as a probabilistic modeling framework of opinion dynamics over time are given by Abir De, Isabel Valera, Niloy Ganguly, Sourangshu Bhattacharya, and Manuel Gomez Rodriguez in ``Learning and Forecasting Opinion Dynamics in Social Networks." In this paper, we will continue to explore their models, now coming from a Bayesian statistical standpoint, specifically looking at the Approximate Bayesian Computation (ABC) method for the computation of better estimations for the data. We will …
Time Series Forecasting And Analysis: A Study Of American Clothing Retail Sales Data, Weijun Huang
Time Series Forecasting And Analysis: A Study Of American Clothing Retail Sales Data, Weijun Huang
Honors Undergraduate Theses
This paper serves to address the effect of time on the sales of clothing retail, from 2010 to May 2019. The data was retrieved from the US Census, where N=113 observations were used, which were plotted to observe their trends. Once outliers and transformations were performed, the best model was fit, and diagnostic review occurred. Inspections for seasonality and forecasting was also conducted. The final model came out to be an ARIMA (2,0,1). Slight seasonality was present, but not enough to drastically influence the trends. Our results serve to highlight the economic growth of clothing retail sales for the past …
Statistical Methods For Mixed Frequency Data Sampling Models, Yun Liu
Statistical Methods For Mixed Frequency Data Sampling Models, Yun Liu
Dissertations, Master's Theses and Master's Reports
The MIDAS models are developed to handle different sampling frequencies in one regression model, preserving information in the higher sampling frequency. Time averaging has been the traditional parametric approach to handle mixed sampling frequencies. However, it ignores information potentially embedded in high frequency. MIDAS regression models provide a concise way to utilize additional information in HF variables. While a parametric MIDAS model provides a parsimonious way to summarize information in HF data, nonparametric models would maintain more flexibility at the expense of the computational complexity. Moreover, one parametric form may not necessarily be appropriate for all cross-sectional subjects. This thesis …