題目：Renewable quantile regression for streaming data sets
時間：2022年10月13日（周四） 09:00-10:30
地點：騰訊會議（636-504-526📯，無密碼）
主講人: 姜榮（東華大學）
摘要：Online updating is an important statistical method for the analysis of big data arriving in streams due to its ability to break the storage barrier and the computational barrier under certain circumstances. The quantile regression, as a widely used regression model in many fields, faces challenges in model fitting and variable selection with big data arriving in streams. Chen et al. (2019, Annals of Statistics) has proposed a quantile regression method for streaming data, but a strong additional condition is required. In this paper, renewable optimized objective functions for regression parameter estimation and variable selection in a quantile regression are proposed. The proposed methods are illustrated using current data and the summary statistics of historical data. Theoretically, the proposed statistics are shown to have the same asymptotic distributions as the standard version computed on an entire data stream with the data batches pooled into one data set, without additional condition. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods.
主講人簡介：姜榮，東華大學統計系副教授。主要研究興趣為大數據建模👩🏼⚖️⇨，分位數回歸和單指標模型等🧖🏽♂️。在Journal of Business & Economic Statistics、Journal of Financial Econometrics🚴🏼♀️、Neurocomputing和Test等國內外著名期刊上發表學術論文二十余篇🕣。主持國家自然科學基金、上海市揚帆計劃等項目多項🙋🏿。
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