48 citations · 209 across the 20 of their papers we have counts for
7 papers · 1 filter
Inference for Low-Rank Models
Victor Chernozhukov, Christian Hansen, Yuan Liao +1
This paper studies inference in linear models with a high-dimensional parameter matrix that can be well-approximated by a ``spiked low-rank matrix.'' A spiked low-rank matrix has r…
Minimax Semiparametric Learning With Approximate Sparsity
Jelena Bradic, Victor Chernozhukov, Whitney K. Newey +1
Estimating linear, mean-square continuous functionals is a pivotal challenge in statistics. In high-dimensional contexts, this estimation is often performed under the assumption of…
How well can we learn large factor models without assuming strong factors?
Yinchu Zhu
In this paper, we consider the problem of learning models with a latent factor structure. The focus is to find what is possible and what is impossible if the usual strong factor co…
Sparsity Double Robust Inference of Average Treatment Effects
Jelena Bradic, Stefan Wager, Yinchu Zhu
Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong "ultra-sparsity" assumptions that may be difficult to val…
Inference for Heterogeneous Effects using Low-Rank Estimation of Factor Slopes
Victor Chernozhukov, Christian Hansen, Yuan Liao +1
We study a panel data model with general heterogeneous effects where slopes are allowed to vary across both individuals and over time. The key dimension reduction assumption we emp…
Testability of high-dimensional linear models with non-sparse structures
Jelena Bradic, Jianqing Fan, Yinchu Zhu
Understanding statistical inference under possibly non-sparse high-dimensional models has gained much interest recently. For a given component of the regression coefficient, we sho…
Two-sample testing in non-sparse high-dimensional linear models
Yinchu Zhu, Jelena Bradic
In analyzing high-dimensional models, sparsity of the model parameter is a common but often undesirable assumption. In this paper, we study the following two-sample testing problem…