Longitudinal data analysis is an essential statistical approach for studying phenomena observed repeatedly over time, allowing researchers to explore both within-subject and between-subject variations ...

Crop insurance is plagued by relatively little historical information but significant spatial information. We investigate the efficacy of using nonparametric Bayesian model averaging (BMA) to ...

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...

Offered through an interdisciplinary partnership, data science at CU Boulder is delivered by the Departments of Applied Mathematics, Computer Science, and Information Science and awarded by the ...

We adapt a semi-Bayesian hierarchical modeling framework to jointly characterize the space–time variability of seasonal precipitation totals and precipitation extremes across the Northern Great Plains ...