“数字+”与之江统计讲坛（第43讲）3月27日上海第二工业大学姜荣教授来中心讲座预告

In this talk, we are concerned with Unconditional Quantile Regression (UQR) method, which has gained significant traction as a popular approach for modeling and analyzing data. However, much like Conditional Quantile Regression (CQR), UQR encounters computational challenges when it comes to obtaining parameter estimates for streaming datasets. This is attributed to the involvement of unknown parameters in the logistic regression loss function used in UQR, which presents obstacles in both computational execution and theoretical development. To address this, we present a novel approach involving smoothing logistic regression estimation. Subsequently, we propose a renewable estimator tailored for UQR with streaming data, relying exclusively on current data and summary statistics derived from historical data. Theoretically, our proposed estimators exhibit equivalent asymptotic properties to the standard version computed directly on the entire dataset, without any additional constraints. Both simulations and real data analysis are conducted to illustrate the finite sample performance of the proposed methods.