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In the research field of big data, one of important issues is how to recoverthe sequentially changing sets of true features when the data sets arrivesequentially. The paper presents a general framework for online updatingvariable selection and parameter estimation in generalized linear models withstreaming datasets. This is a type of online updating penalty likelihoods withdifferentiable or non-differentiable penalty function. The online updatingcoordinate descent algorithm is proposed to solve the online updatingoptimization problem. Moreover, a tuning parameter selection is suggested in anonline updating way. The selection and estimation consistencies, and the oracleproperty are established, theoretically. Our methods are further examined andillustrated by various numerical examples from both simulation experiments anda real data analysis.

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