Robust Estimation of Heterogeneity in Factorial Data using Rashomon Partitions
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.
Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model
SRLDA integrates spectral correction and regularization for optimal classification under spiked models.
Differentially Private Theil-Sen Estimator Analysis for Simple Linear Regression
Differentially private algorithms like DPTheilSen offer accurate and private linear regression estimates.
Heteroskedastic PCA with Missing Data: Inference and Uncertainty Quantification
Valid inference on principal subspace and covariance matrix under heteroskedastic noise with missing data.
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