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stat.ME2025
Divide-and-conquer with finite sample sizes: valid and efficient possibilistic inference
Emily C. Hector, Leonardo Cella, Ryan Martin
Divide-and-conquer methods use large-sample approximations to provide frequentist guarantees when each block of data is both small enough to facilitate efficient computation and la…
stat.CO2025
Variational empirical Bayes variable selection in high-dimensional logistic regression
Yiqi Tang, Ryan Martin
Logistic regression involving high-dimensional covariates is a practically important problem. Often the goal is variable selection, i.e., determining which few of the many covariat…
math.ST2025
The typicality principle and its implications for statistics and data science
Yiran Jiang, Zeyu Zhang, Ryan Martin +1
A central focus of data science is the transformation of empirical evidence into knowledge. As such, the key insights and scientific attitudes of deep thinkers like Fisher, Popper,…