arXiv subjects

James Molyneux

Publications and source records attributed to James Molyneux.

3 recordsLinked to original sources

arXiv2024

The Importance of Discussing Assumptions when Teaching Bootstrapping

Bootstrapping and other resampling methods are increasingly appearing in the textbooks and curricula of courses that introduce undergraduate students to statistical methods. In order to teach the bootstrap well, students and instructors need to be aware of the assumptions behind these intervals. In this article we discuss important assumptions about simple non-parametric bootstrap intervals and their corresponding hypothesis tests. We present simulations that instructors can use to help students understand some of the assumptions behind these methods. The simulations will be especially relevant to instructors who desire to increase accessibility for students from non-mathematical backgrounds, including those with math anxiety.

stat.OT↗

arXiv2022

Estimating Excess COVID-19 Infections with Nonparametric Self-Exciting Point Processes

The COVID-19 pandemic has led to a vast amount of growth for statistical models and methods which characterize features of disease outbreaks. One class of models that came to light in this regard has been the use of self-exciting point processes, wherein infections occur both "at random" and also more systematically from person-to-person transmission. Beyond the modeling of the overall COVID-19 outbreak, the pandemic has also motivated research assessing various policy decisions and event outcomes. One such area of study, addressed here, relates to the formulation of methods which measure the impact that large events or gatherings of people had in the local areas where the events were held. We formulate an alternative approach to traditional causal inference methods and then apply our method to assessing the impact that then President Donald Trump's re-election campaign rallies had on COVID-19 infections in areas where the rallies were hosted. By incorporating several adaptions to nonparametric self-exciting point process models, we estimate both the excess number of COVID-19 infections brought on by the rallies and the duration of time in which these excess infections persisted.

stat.AP↗

arXiv2020

Assessing the contagiousness of mass shootings with nonparametric Hawkes processes

Gun violence and mass shootings are high-profile epidemiological issues facing the United States with questions regarding their contagiousness gaining prevalence in news media. Through the use of nonparametric Hawkes processes, we examine the evidence for the existence of contagiousness within a catalog of mass shootings and highlight the broader benefits of using such nonparametric point process models in modeling the occurrence of such events.

stat.AP↗