معرفی
Ben Sherwood is an Associate Professor and Jack and Shirley Howard Mid-Career Professor in the Analytics, Information, Operations academic area at the University of Kansas School of Business. His research focuses on developing advanced statistical methodologies with applications across various domains including healthcare, finance, and business analytics.
- Ph.D. in Statistics, University of Minnesota, 2014
- B.A. in Mathematics and Computer Science, Macalester College, 2003
- Post Doctoral Fellow at Johns Hopkins Biostatistics Department, 2014-2016
Sherwood's research primarily centers on quantile regression methodologies, with special emphasis on penalized approaches for high-dimensional data. His work extends to semiparametric regression, multivariate regression models, and addressing challenges with missing data. He develops statistical methods with practical applications in business problems, healthcare analytics, and genomic studies. His approach often involves creating new statistical techniques, implementing them in software, and providing theoretical foundations through mathematical proofs.
His publication record shows a consistent trajectory of impactful research, with recent work focusing on quantile regression for equity premium prediction, Bayesian network applications for PTSD screening, and advanced techniques for model selection in high-dimensional settings. His research bridges theoretical statistics with practical applications, particularly in business analytics contexts.
Sherwood actively mentors PhD students, encouraging them to develop novel statistical methods while maintaining flexibility in their research direction. He frequently co-advises students with faculty from related disciplines including Professors Prakash Shenoy, Karthik Srinivasan, and Shaobo Li. His students have worked on diverse projects including beta regression for model selection uncertainty, Bayesian networks for PTSD prediction in veterans, bankruptcy prediction for firms, and analyzing crowdfunding platform dynamics.
Complementing his theoretical work, Sherwood has developed multiple software packages to implement his methodologies, including rqPen for penalized quantile regression, hrqglas for group variable selection, and mcen for multivariate cluster elastic net models. These tools make advanced statistical methods accessible to practitioners across various fields.
