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20212025
most citedGeneralized Data Thinning Using Sufficient Statistics

16 citations · 45 across the 7 of their papers we have counts for

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stat.ME2025

Thinning a Wishart Random Matrix

Ameer Dharamshi, Anna Neufeld, Lucy L. Gao +2

Recent work has explored data thinning, a generalization of sample splitting that involves decomposing a (possibly matrix-valued) random variable into independent components. In th…

stat.ME2024★ 3 cited

Decomposing Gaussians with Unknown Covariance

Ameer Dharamshi, Anna Neufeld, Lucy L. Gao +2

Common workflows in machine learning and statistics rely on the ability to partition the information in a data set into independent portions. Recent work has shown that this may be…

stat.ME2024

Discussion of "Data fission: splitting a single data point"

Anna Neufeld, Ameer Dharamshi, Lucy L. Gao +2

Leiner et al. [2023] introduce an important generalization of sample splitting, which they call data fission. They consider two cases of data fission: P1 fission and P2 fission. Wh…

stat.ME2023★ 16 cited

Generalized Data Thinning Using Sufficient Statistics

Ameer Dharamshi, Anna Neufeld, Keshav Motwani +3

Our goal is to develop a general strategy to decompose a random variable into multiple independent random variables, without sacrificing any information about unknown parameter…

stat.ME2023★ 14 cited

Data thinning for convolution-closed distributions

Anna Neufeld, Ameer Dharamshi, Lucy L. Gao +1

We propose data thinning, an approach for splitting an observation into two or more independent parts that sum to the original observation, and that follow the same distribution as…

stat.ME2022★ 2 cited

Inference after latent variable estimation for single-cell RNA sequencing data

Anna Neufeld, Lucy L. Gao, Joshua Popp +2

In the analysis of single-cell RNA sequencing data, researchers often characterize the variation between cells by estimating a latent variable, such as cell type or pseudotime, rep…

stat.ME2021★ 10 cited

Tree-Values: selective inference for regression trees

Anna C. Neufeld, Lucy L. Gao, Daniela M. Witten

We consider conducting inference on the output of the Classification and Regression Tree (CART) [Breiman et al., 1984] algorithm. A naive approach to inference that does not accoun…