4 papers
Inverse Probability Weighting in a Post-Bayesian World
Owen Thomas, William Denault, Valeria Vitelli
We present a justification of the use of Inverse Probability Weighting (IPW) in a post-Bayesian framework, in which the bias-correction provided by IPW in a frequentist context is…
Covariate-moderated Empirical Bayes Matrix Factorization
William R. P. Denault, Karl Tayeb, Peter Carbonetto +2
Matrix factorization is a fundamental method in statistics and machine learning for inferring and summarizing structure in multivariate data. Modern data sets often come with "side…
Nash: Neural Adaptive Shrinkage for Structured High-Dimensional Regression
William R. P. Denault
Sparse linear regression is a fundamental tool in data analysis. However, traditional approaches often fall short when covariates exhibit structure or arise from heterogeneous sour…
Wavelet Screaming: a novel approach to analyzing GWAS data
William Denault, Håkon K. Gjessing, Julius Juodakis +2
We present an alternative method for genome-wide association studies (GWAS) that is more powerful than the regular GWAS method for locus detection. The regular GWAS method suffers…