Communication-Efficient Pilot Estimation for Non-Randomly Distributed Data in Diverging Dimensions

发布时间：2026-10-09 作者：浏览次数：

Speaker:	夏小超	DateTime:	10月17日（周六）上午9：30 - 11：00
Brief Introduction to Speaker: 夏小超，重庆大学		Place:	6号楼C405
Abstract:The communication-efficient surrogate likelihood (CSL) framework \citep{jordan2019communication} is notable for handling massive or distributed datasets. The CSL methods use the first machine as the central one for optimization with its data and assume a fixed dimension for statistical properties. However, CSL may not suit non-randomly or heterogeneously distributed data and limit its applicability to diverging- or high-dimensional datasets. To address these issues, we propose a communication-efficient pilot (CEP) estimation strategy. This involves pilot sampling on each machine to create a pilot sample dataset and using a new pilot sample-based surrogate loss to approximate the global one, with the minimizer termed the CEP estimator. We rigorously investigate theoretical properties of the CEP estimator including its convergence rate, reaching the global rate $\sqrt{\frac{p_n}{N}}$, and its asymptotic normality when the dimension $p_n$ diverges with the pilot sample size $r$ and $p_...