Journal article
Online two-way estimation and inference via linear mixed-effects models
Statistics in medicine, Vol.41(25), pp.5113-5133
08/19/2022
DOI: 10.1002/sim.9557
Abstract
In this article, we tackle the estimation and inference problem of analyzing distributed streaming data that is collected continuously over multiple data sites. We propose an online two-way approach via linear mixed-effects models. We explicitly model the site-specific effects as random-effect terms, and tackle both between-site heterogeneity and within-site correlation. We develop an online updating procedure that does not need to re-access the previous data and can efficiently update the parameter estimate, when either new data sites, or new streams of sample observations of the existing data sites, become available. We derive the non-asymptotic error bound for our proposed online estimator, and show that it is asymptotically equivalent to the offline counterpart based on all the raw data. We compare with some key alternative solutions both analytically and numerically, and demonstrate the advantages of our proposal. We further illustrate our method with two data applications.
Details
- Title: Subtitle
- Online two-way estimation and inference via linear mixed-effects models
- Creators
- Lan Luo - University of IowaLexin Li - University of California, Berkeley
- Resource Type
- Journal article
- Publication Details
- Statistics in medicine, Vol.41(25), pp.5113-5133
- DOI
- 10.1002/sim.9557
- ISSN
- 0277-6715
- eISSN
- 1097-0258
- Grant note
- DOI: 10.13039/100000143, name: Division of Computing and Communication Foundations, award: CIF‐2102227; DOI: 10.13039/100000002, name: National Institutes of Health, award: R01AG061303
- Language
- English
- Date published
- 08/19/2022
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984296993802771
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