12 citations · 13 across the 5 of their papers we have counts for
5 papers
Differentially Private Inference for Longitudinal Linear Regression
Getoar Sopa, Marco Avella Medina, Cynthia Rush
Differential Privacy (DP) provides a rigorous framework for releasing statistics while protecting individual information present in a dataset. Although substantial progress has bee…
Statistical Guarantees for Data-driven Posterior Tempering
Ruchira Ray, Marco Avella Medina, Cynthia Rush
Posterior tempering reduces the influence of the likelihood in the calculation of the posterior by raising the likelihood to a fractional power . The resulting power posterior -…
A theoretical framework for M-posteriors: frequentist guarantees and robustness properties
Juraj Marusic, Marco Avella Medina, Cynthia Rush
We provide a theoretical framework for a wide class of generalized posteriors that can be viewed as the natural Bayesian posterior counterpart of the class of M-estimators in the f…
Asymptotics for power posterior mean estimation
Ruchira Ray, Marco Avella Medina, Cynthia Rush
Power posteriors "robustify" standard Bayesian inference by raising the likelihood to a constant fractional power, effectively downweighting its influence in the calculation of the…
Data Visualization on Day One: Bringing Big Ideas into Intro Stats Early and Often
Xiaofei Wang, Cynthia Rush, Nicholas Jon Horton
In a world awash with data, the ability to think and compute with data has become an important skill for students in many fields. For that reason, inclusion of some level of statis…