Online updating mode learning for streaming datasets

Abstract

In the case of limited computer memory, it is worth discussing how to regression the big data stream and solve the outlier problem reasonably. To solve the above problems, this article proposes an online updatable modal linear regression algorithm, which is robust to heavy-tailed distribution, heterogeneous errors and outliers. However, due to streaming data, the traditional static statistical methods are facing new challenges. In addition, existing inference tools in online learning cannot be used directly for modal linear regression. A major challenge is that the data is one-pass and the final amount of data is not even known, which results in the inability to obtain optimal bandwidth, resulting in poor theoretical performance of the estimator. In particular, the estimation and inference methods are proposed. The process only passes the data once. Finally, the finite sample performance of the proposed method is verified by simulation and real data analysis.