184 citations · 449 across the 32 of their papers we have counts for
4 papers · 1 filter
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…
Linear Hypothesis Testing in Dense High-Dimensional Linear Models
Yinchu Zhu, Jelena Bradic
We propose a methodology for testing linear hypothesis in high-dimensional linear models. The proposed test does not impose any restriction on the size of the model, i.e. model spa…
Significance testing in non-sparse high-dimensional linear models
Yinchu Zhu, Jelena Bradic
In high-dimensional linear models, the sparsity assumption is typically made, stating that most of the parameters are equal to zero. Under the sparsity assumption, estimation and,…
Robust Confidence Intervals in High-Dimensional Left-Censored Regression
Jelena Bradic, Jiaqi Guo
This paper develops robust confidence intervals in high-dimensional and left-censored regression. Type-I censored regression models are extremely common in practice, where a compet…