6 papers
Precise Asymptotics for Dense High-Dimensional Huber Regression under Heteroscedasticity
Jing Zhou, Ramji Venkataramanan, Hui Zou
We study Huber regression in a heteroscedastic linear model in the proportional regime . Our main result is an exact characterization of the asymptotic em…
Agent-Kernel: A MicroKernel Multi-Agent System Framework for Adaptive Social Simulation Powered by LLMs
Yuren Mao, Peigen Liu, Xinjian Wang +11
Multi-Agent System (MAS) developing frameworks serve as the foundational infrastructure for social simulations powered by Large Language Models (LLMs). However, existing frameworks…
Overcoming global sensitivity limitations: using active subspaces to explore discrepancies between global and local parameter sensitivities
Huiyan Zou, Allison L. Lewis
Global sensitivity metrics are essential tools for assessing parameter importance in complex models, particularly when precise information about parameter values is unavailable. In…
Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data
Yifan Gu, Hanfang Yang, Songshan Yang +1
In modern data analysis, statistical efficiency improvement is expected via effective collaboration among multiple data holders with non-shared data. In this article, we propose a…
A Non-Parametric Box-Cox Approach to Robustifying High-Dimensional Linear Hypothesis Testing
He Zhou, Hui Zou
The mainstream theory of hypothesis testing in high-dimensional regression typically assumes the underlying true model is a low-dimensional linear regression model, yet the Box-Cox…
High-dimensional Newey-Powell Test Via Approximate Message Passing
Jing Zhou, Hui Zou
We propose a high-dimensional extension of the heteroscedasticity test proposed in Newey and Powell (1987). Our test is based on expectile regression in the proportional asymptotic…