A General Framework of Online Updating Variable Selection for Generalized Linear Models with Streaming Datasets

TL;DR

This paper introduces a comprehensive online updating framework for variable selection and parameter estimation in generalized linear models, effectively handling streaming data with changing feature sets, supported by theoretical guarantees and practical experiments.

Contribution

It proposes a novel online updating coordinate descent algorithm with tuning parameter selection, establishing consistency and oracle properties for streaming data scenarios.

Findings

Method achieves accurate feature recovery in simulations.

The approach demonstrates strong theoretical guarantees.

Numerical experiments validate effectiveness on real datasets.

Abstract

In the research field of big data, one of important issues is how to recover the sequentially changing 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 penalty likelihoods with differentiable or non-differentiable penalty function. The online updating coordinate descent algorithm is proposed to solve 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.