A novel approach of empirical likelihood with massive data

2023/03/13 by Yang Liu, Xia Chen, Liu, Yang +3
Computer Science · Mathematics · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2303.07259

openalex publication_date 2023/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we propose a novel approach for tackling the obstacles of empirical likelihood in the face of massive data, which is called split sample mean empirical likelihood (SSMEL), our approach provides a unique perspective for solving big data problems. We show that the SSMEL estimator has the same estimation efficiency as the empirical likelihood estimator with the full dataset, and maintains the important statistical property of Wilks' theorem, allowing our proposed approach to be used for statistical inference without estimating the covariance matrix. This effectively tackles the hurdle of the Divide and Conquer (DC) algorithm for statistical inference. We further illustrate the proposed approach via simulation studies and real data analysis.

Related

• Statistical inference in massive datasets by empirical likelihood
• Nonparametric inference with massive data via grouped empirical likelihood
• Split Sample Empirical Likelihood
• Piecewise Empirical Likelihood
• Composite Empirical Likelihood