6 papers
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…
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…
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,…
Regularized e-processes: anytime valid inference with knowledge-based efficiency gains
Ryan Martin
Classical statistical methods have theoretical justification when the sample size is predetermined. In applications, however, it's often the case that sample sizes are data-depende…
Which statistical hypotheses are afflicted with false confidence?
Ryan Martin
The false confidence theorem establishes that, for any data-driven, precise-probabilistic method for uncertainty quantification, there exists (non-trivial) false hypotheses to whic…
Fisher's underworld and the behavioral-statistical reliability balance in scientific inference
Ryan Martin
That science and other domains are now largely data-driven means virtually unlimited opportunities for statisticians. With great power comes responsibility, so it's imperative that…