James E. Johndrow

James Johndrow, Aaron Smith, Natesh Pillai, David B. Dunson (2019), MCMC for Imbalanced Categorical Data, Journal of the American Statistical Association, (to appear).

James Johndrow and Anirban Bhattacharya (2019), Optimal Gaussian Approximations to the Posterior for Log-Linear Models with Diaconis–Ylvisaker Priors, Bayesian Analysis, 13 (1), pp. 201-223.

James Johndrow, Kristian Lum, Daniel Manrique-Vallier (2019), Low-risk population size estimates in the presence of capture heterogeneity, Biometrika, 106 (1), pp. 197-210.

James Johndrow and Kristian Lum (2019), An algorithm for removing sensitive information: Application to race-independent recidivism prediction, Annals of Applied Statistics, 13 (1), pp. 189-220.

James Johndrow, Anirban Bhattacharya, David B. Dunson (2019), Tensor decompositions and sparse log-linear models, The Annals of Statistics, 45 (1), pp. 1-38.

James Johndrow and Julia A. Palacios (2019), Exact limits of inference in coalescent models, Theoretical Population Biology, 125, pp. 75-93.

Emanuele Aliverti, Kristian Lum, James Johndrow, David B. Dunson (Working), Removing the influence of a group variable in high-dimensional predictive modelling.

James Johndrow and Aaron Smith (2018), Fast mixing of metropolis-hastings with unimodal targets, Electronic Communications in Probability, 23 (71), pp. 1-9.

James Johndrow, Paulo Orenstein, Anirban Bhattacharya (Working), Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a Scalable MCMC Algorithm for the Horseshoe Prior.

Leo L. Duan, James Johndrow, David B. Dunson (2018), Scaling up Data Augmentation MCMC via Calibration, Journal of Machine Learning Research, 19, pp. 1-34.