Real-Time Regression Analysis of Streaming Clustered Data With Possible Abnormal Data Batches

Lan Luo; Ling Zhou; Peter X.-K. Song

doi:10.1080/01621459.2022.2026778

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Real-Time Regression Analysis of Streaming Clustered Data With Possible Abnormal Data Batches

Journal article

Peer reviewed

Real-Time Regression Analysis of Streaming Clustered Data With Possible Abnormal Data Batches

Lan Luo, Ling Zhou and Peter X.-K. Song

Journal of the American Statistical Association, Vol.118(543), pp.2029-2044

2023

DOI: 10.1080/01621459.2022.2026778

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https://pmc.ncbi.nlm.nih.gov/articles/PMC10530766/pdf/nihms-1813981.pdfView

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Abstract

This article develops an incremental learning algorithm based on quadratic inference function (QIF) to analyze streaming datasets with correlated outcomes such as longitudinal data and clustered data. We propose a renewable QIF (RenewQIF) method within a paradigm of renewable estimation and incremental inference, in which parameter estimates are recursively renewed with current data and summary statistics of historical data, but with no use of any historical subject-level raw data. We compare our renewable estimation method with both offline QIF and offline generalized estimating equations (GEE) approach that process the entire cumulative subject-level data all together, and show theoretically and numerically that our renewable procedure enjoys statistical and computational efficiency. We also propose an approach to diagnose the homogeneity assumption of regression coefficients via a sequential goodness-of-fit test as a screening procedure on occurrences of abnormal data batches. We implement the proposed methodology by expanding existing Spark's Lambda architecture for the operation of statistical inference and data quality diagnosis. We illustrate the proposed methodology by extensive simulation studies and an analysis of streaming car crash datasets from the National Automotive Sampling System-Crashworthiness Data System (NASS CDS). Supplementary materials for this article are available online.

Abnormal data batch detection

Generalized estimating equation

Incremental statistical analysis

Online learning

Quadratic inference function

Spark computing platform

Details

Title: Subtitle: Real-Time Regression Analysis of Streaming Clustered Data With Possible Abnormal Data Batches
Creators: Lan Luo - University of Iowa, Statistics and Actuarial Science
Ling Zhou - Southwestern University of Finance and Economics
Peter X.-K. Song - University of Michigan
Resource Type: Journal article
Publication Details: Journal of the American Statistical Association, Vol.118(543), pp.2029-2044
DOI: 10.1080/01621459.2022.2026778
ISSN: 0162-1459
eISSN: 1537-274X
Publisher: Taylor & Francis
Grant note: DOI: 10.13039/100000001, name: National Science Foundation, award: DMS1811734, DMS2113564; name: Song, and the National Natural Science, award: 11901470, 11931014, 11829101; DOI: 10.13039/501100012226, name: Fundamental Research Funds for the Central Universities, award: JBK190904, JBK1806002; DOI: 10.13039/100000121, name: Division of Mathematical Sciences; DOI: 10.13039/100000066, name: National Institute of Environmental Health Sciences; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China
Language: English
Electronic publication date: 03/14/2022
Date published: 2023
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9984257628802771

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