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Found 4 papers, 1 papers with code
Nonparametric Empirical Bayes Estimation and Testing for Sparse and Heteroscedastic Signals
In contrast to the state-of-the-art methods, the proposed methods solve the estimation and testing problem at once with several merits: 1) an accurate sparsity estimation; 2) point estimates with shrinkage/soft-thresholding property; 3) credible intervals for uncertainty quantification; 4) an optimal multiple testing procedure that controls false discovery rate.
Estimation of a score-explained non-randomized treatment effect in fixed and high dimensions
In this paper, we present a new model coined SCENTS: Score Explained Non-Randomized Treatment Systems, and a corresponding method that allows estimation of the treatment effect at $\sqrt{n}$ rate in the presence of fairly general forms of confoundedness, when the `score' variable on whose basis treatment is assigned can be explained via certain feature measurements of the individuals under study.
Methodology Statistics Theory Statistics Theory
Inference In High-dimensional Single-Index Models Under Symmetric Designs
The problem of statistical inference for regression coefficients in a high-dimensional single-index model is considered.
Statistics Theory Other Statistics Statistics Theory
On conditional parity as a notion of non-discrimination in machine learning
We identify conditional parity as a general notion of non-discrimination in machine learning.
