@article{weishampel_staicu_rand_2023, title={Classification of social media users with generalized functional data analysis}, volume={179}, ISSN={["1872-7352"]}, url={https://doi.org/10.1016/j.csda.2022.107647}, DOI={10.1016/j.csda.2022.107647}, abstractNote={Technological advancement has made possible the collection of data from social media platforms at unprecedented speed and volume. Current methods for analyzing such data lack interpretability, are computationally intensive, or require a rigid data specification. Functional data analysis enables the development of a flexible, yet interpretable, modeling framework to extract lower-dimensional relevant features of a user's posting behavior on social media, based on their posting activity over time. The extracted features can then be used to discriminate a malicious user from a genuine one. The proposed methodology can classify a binary time series in a computationally efficient manner and provides more insights into the posting behavior of social media agents. Performance of the method is illustrated numerically in simulation studies and on a motivating Twitter data set. The developed methods are applicable to other social media data, such as Facebook, Instagram, Reddit, or TikTok, or any form of digital interaction where the user's posting behavior is indicative of their user class.}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, author={Weishampel, Anthony and Staicu, Ana -Maria and Rand, William}, year={2023}, month={Mar} }
 @article{wells_dolwick_eder_evangelista_foley_mannshardt_misenis_weishampel_2021, title={Improved estimation of trends in US ozone concentrations adjusted for interannual variability in meteorological conditions}, volume={248}, ISSN={["1873-2844"]}, DOI={10.1016/j.atmosenv.2021.118234}, abstractNote={Daily maximum 8-hour average (MDA8) ozone (O3) concentrations are well-known to be influenced by local meteorological conditions, which vary across both daily and seasonal temporal scales. Previous studies have adjusted long-term trends in O3 concentrations for meteorological effects using various statistical and mathematical methods in order to get a better estimate of the long-term changes in O3 concentrations due to changes in precursor emissions such as nitrogen oxides (NOX) and volatile organic compounds (VOCs). In this work, the authors present improvements to the current method used by the United States Environmental Protection Agency (US EPA) to adjust O3 trends for meteorological influences by making refinements to the input data sources and by allowing the underlying statistical model to vary locally using a variable selection procedure. The current method is also expanded by using a quantile regression model to adjust trends in the 90th and 98th percentiles of the distribution of MDA8 O3 concentrations, allowing for a better understanding of the effects of local meteorology on peak O3 levels in addition to seasonal average concentrations. The revised method is used to adjust trends in the May to September mean, 90th percentile, and 98th percentile MDA8 O3 concentrations at over 700 monitoring sites in the U.S. for years 2000 to 2016. The utilization of variable selection and quantile regression allow for a more in-depth understanding of how weather conditions affect O3 levels in the U.S. This represents a fundamental advancement in our ability to understand how interannual variability in weather conditions in the U.S. may impact attainment of the O3 National Ambient Air Quality Standards (NAAQS).}, journal={ATMOSPHERIC ENVIRONMENT}, author={Wells, Benjamin and Dolwick, Pat and Eder, Brian and Evangelista, Mark and Foley, Kristen and Mannshardt, Elizabeth and Misenis, Chris and Weishampel, Anthony}, year={2021}, month={Mar} }
 @article{overgoor_chica_rand_weishampel_2019, title={Letting the Computers Take Over: Using AI to Solve Marketing Problems}, volume={61}, ISSN={["2162-8564"]}, url={https://publons.com/wos-op/publon/37201971/}, DOI={10.1177/0008125619859318}, abstractNote={ Artificial intelligence (AI) has proven to be useful in many applications from automating cars to providing customer service responses. However, though many firms want to take advantage of AI to improve marketing, they lack a process by which to execute a Marketing AI project. This article discusses the use of AI to provide support for marketing decisions. Based on the established Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, it creates a process for managers to use when executing a Marketing AI project and discusses issues that might arise. It explores how this framework was used to develop three cutting-edge Marketing AI applications. }, number={4}, journal={CALIFORNIA MANAGEMENT REVIEW}, author={Overgoor, Gijs and Chica, Manuel and Rand, William and Weishampel, Anthony}, year={2019}, month={Aug}, pages={156–185} }