Renewable learning for multiplicative regression with streaming datasets

When large amounts of data continuously arrive in streams, online updating is an effective way to reduce storage and computational burden. The key idea that the previous estimators are sequentially updated only using current summary statistics historical data. In this article, we develop a renewable learning method for multiplicative regression model with streaming data, where parameter estimator based on least product relative error (LPRE) criterion renewed without revisiting Under some regularity conditions, establish consistency asymptotic normality estimator. Moreover, our proposed has identical distribution full LPRE Numerical studies two real-world datasets provided evaluate performance method.