arXiv subjects

Chunjing Li

Publications and source records attributed to Chunjing Li.

3 recordsLinked to original sources

arXiv2026

Distributed Convolutional Rank Regression over Decentralized Networks

This paper studies convolution rank regression (CRR) over decentralized distributed learning networks. We propose a novel decentralized CRR framework, in which estimators are obtained by solving consensus-constrained optimization with kernel-smoothed rank loss. The developed estimation scheme relies solely on local node data and information shared by neighboring nodes, thereby achieving privacy preservation and high communication efficiency. For heterogeneous network settings, we establish finite-sample error bounds for the decentralized CRR estimator and derive exact support recovery guarantees for the sparse decentralized CRR Lasso estimator. To facilitate numerical implementation, we adopt a generalized consensus ADMM to efficiently solve local subproblems across all network nodes. We verify the favorable performance of our developed approach via extensive numerical simulations and real-data experiments.

stat.ME↗

arXiv2026

Optimal Poisson subsampling for quantile regression with large-scale longitudinal data

To address the computational challenges arising from large-scale longitudinal data, an optimal Poisson subsampling algorithm is proposed for quantile regression. The proposed method can substantially alleviate computational burden. Under some regularity conditions, we derive the asymptotic properties of the estimators from weighted quantile generalized estimating equations. For practical implementation, an efficient algorithm is proposed for parameter estimation. Furthermore, asymptotic theory is established for penalized weighted smooth quantile generalized estimating equations, and regularized parameter estimation is performed within the optimal Poisson subsampling framework. Both numerical simulations and a real data application demonstrate that the proposed optimal Poisson subsampling algorithm outperforms the uniform Poisson subsampling algorithm, and the regularized estimation exhibits satisfactory performance as well.

stat.CO↗

arXiv2025

Optional subsampling for generalized estimating equations in growing-dimensional longitudinal Data

As a powerful tool for longitudinal data analysis, the generalized estimating equations have been widely studied in the academic community. However, in large-scale settings, this approach faces pronounced computational and storage challenges. In this paper, we propose an optimal Poisson subsampling algorithm for generalized estimating equations in large-scale longitudinal data with diverging covariate dimension, and establish the asymptotic properties of the resulting estimator. We further derive the optimal Poisson subsampling probability based on A- and L-optimality criteria. An approximate optimal Poisson subsampling algorithm is proposed, which adopts a two-step procedure to construct these probabilities. Simulation studies are conducted to evaluate the performance of the proposed method under three different working correlation matrices. The results show that the method remains effective even when the working correlation matrices are misspecified. Finally, we apply the proposed method to the CHFS dataset to illustrate its empirical performance.

stat.CO↗