A general framework of online updating variable selection for generalized linear models with streaming datasets

In the era of big data, one of the important issues is how to recover the sets of true features when the data sets arrive sequentially. The paper presents a general framework for online updating variable selection and parameter estimation in generalized linear models with streaming datasets. This is...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 2023-02, Vol.93 (3), p.325-340
Main Authors: Ma, Xiaoyu, Lin, Lu, Gai, Yujie
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Data analysis
Datasets
online updating criterion
Optimization
oracle property
Parameter estimation
penalized likelihoods
Penalty function
Statistical models
Streaming data environment
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Summary:In the era of big data, one of the important issues is how to recover the sets of true features when the data sets arrive sequentially. The paper presents a general framework for online updating variable selection and parameter estimation in generalized linear models with streaming datasets. This is a type of online updating penalized likelihoods with differentiable or non-differentiable penalty functions. An online updating coordinate descent algorithm is proposed for solving the online updating optimization problem. Moreover, a tuning parameter selection is suggested in an online updating way. The selection and estimation consistencies and the oracle property are established, theoretically. Our methods are further examined and illustrated by various numerical examples from both simulation experiments and a real data analysis.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2022.2107207