[2309.09872] A Moment-assisted Approach for Improving Subsampling-based MLE with Large-scale data Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Methodology arXiv:2309.09872 (stat) [Submitted on 18 Sep 2023 ( v1 ), last revised 24 Apr 2026 (this version, v4)] Title: A Moment-assisted Approach for Improving Subsampling-based MLE with Large-scale data Authors: Miaomiao Su , Qihua Wang , Ruoyu Wang View a PDF of the paper titled A Moment-assisted Approach for Improving Subsampling-based MLE with Large-scale data, by Miaomiao Su and 2 other authors View PDF HTML (experimental) Abstract: The maximum likelihood estimation is computationally demanding for large datasets, particularly when the likelihood function includes integrals. Subsampling can reduce the computational burden, but it often results in efficiency this http URL paper proposes a moment-assisted subsampling (MAS) method that can improve the estimation efficiency of existing subsampling-based maximum likelihood this http URL motivation behind this approach stems from the fact that sample moments can be efficiently computed even if the sample size of the whole data set is this http URL the generalized method of moments, the proposed method incorporates informative sample moments of the whole data. The MAS estimator can be computed rapidly and is asymptotically normal with a smaller asymptotic variance than the corresponding estimator without incorporating sample moments of the whole data. The asymptotic variance of the proposed estimator depends on the specific sample moments incorporated. We derive the optimal moment that minimizes the resulting asymptotic variance in terms of Loewner order. The proposed MAS estimator can achieve the same estimation efficiency as the whole data-based estimator when the optimal moment is incorporated. Numerical results demonstrate the promising performance of the proposed method in both estimation and computational efficiency compared with existing subsampling methods. Subjects: Methodology (stat.ME) Cite as: arXiv:2309.09872 [stat.ME] (or arXiv:2309.09872v4 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2309.09872 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Miaomiao Su [ view email ] [v1] Mon, 18 Sep 2023 15:31:53 UTC (397 KB) [v2] Sat, 20 Jul 2024 15:49:31 UTC (159 KB) [v3] Sun, 11 Aug 2024 01:18:42 UTC (152 KB) [v4] Fri, 24 Apr 2026 14:39:16 UTC (159 KB) Full-text links: Access Paper: View a PDF of the paper titled A Moment-assisted Approach for Improving Subsampling-based MLE with Large-scale data, by Miaomiao Su and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ME < prev | next > new | recent | 2023-09 Change to browse by: stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from