18 citations · 26 across the 12 of their papers we have counts for
5 papers · 1 filter
When is it worthwhile to jackknife? Breaking the quadratic barrier for Z-estimators
Licong Lin, Fangzhou Su, Wenlong Mou +2
Resampling methods are especially well-suited to inference with estimators that provide only "black-box'' access. Jackknife is a form of resampling, widely used for bias correction…
Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models
Michael Celentano, Zhou Fan, Licong Lin +1
We study mean-field variational inference in a Bayesian linear model when the sample size n is comparable to the dimension p. In high dimensions, the common approach of minimizing…
Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference
Licong Lin, Mufang Ying, Suvrojit Ghosh +2
Estimation and inference in statistics pose significant challenges when data are collected adaptively. Even in linear models, the Ordinary Least Squares (OLS) estimator may fail to…
Semi-parametric inference based on adaptively collected data
Licong Lin, Koulik Khamaru, Martin J. Wainwright
Many standard estimators, when applied to adaptively collected data, fail to be asymptotically normal, thereby complicating the construction of confidence intervals. We address thi…
Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control
Taejoo Ahn, Licong Lin, Song Mei
In high dimensional variable selection problems, statisticians often seek to design multiple testing procedures that control the False Discovery Rate (FDR), while concurrently iden…