The core message of this paper is to develop a robust framework called Rashomon Partitions to estimate and analyze heterogeneity in factorial data, where the outcome of interest varies with combinations of observable covariates. The proposed approach enumerates a set of high posterior probability partitions that offer substantively different explanations for the heterogeneity, allowing for robust conclusions that are not overly sensitive to the choice of a single "optimal" partition.
SRLDA integrates spectral correction and regularization for optimal classification under spiked models.
Differentially private algorithms like DPTheilSen offer accurate and private linear regression estimates.
Valid inference on principal subspace and covariance matrix under heteroskedastic noise with missing data.