| 流数据下的复合分位数回归 |
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| 引用本文：韩星敏,姜荣.流数据下的复合分位数回归[J].上海第二工业大学(中文版),2024,41(2):208-217 |
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| 中文摘要:随着互联网的发展, 数据规模急剧增长, 但有限的内存只能存储一小批数据, 因此在不访问历史数据的情况下进行分析是非常有必要的, 流数据分析也因而引起了广泛关注。同时复合分位数回归因其鲁棒性和全面性, 在许
多领域得到应用, 但由于传统复合分位数回归是基于内存可容纳完整数据的条件, 因此在流数据环境中实现复合分位数回归是非常有挑战的。针对流数据提出了一种可更新的复合分位数回归方法, 可以随着数据的到达, 使用当前
数据和历史数据的汇总统计量来更新估计量。在理论上证明提出的可更新估计量与使用完整数据得到的估计量是渐近等价的。最后通过模拟研究验证了所提出方法的有效性。 |
| 中文关键词:复合分位数回归 流数据 在线可更新估计方程 |
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| Renewable Composite Quantile Regression for Streaming Data Sets |
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| Abstract:With the development of the Internet, the scale of data has grown dramatically. However, due to limited memory capacity that can only store a small batch of data, it is essential to analyze data without accessing historical data. Consequently, streaming data analysis has attracted widespread attention. Meanwhile, composite quantile regression, known for its robustness and and comprehensiveness, has been applied in various fields. However, implementing composite quantile regression for streaming data is challenging since traditional methods are based on the condition that the entire dataset can fit into memory. An updating composite quantile regression method specifically is designed for streaming data. The estimates can be updated as the data arrives using both current data and the summary
statistics of historical data. In theory, it is proven that the updatable estimator proposed is asymptotically equivalent to the estimator obtained using complete data. Finally, the effectiveness of the proposed method is verified through simulation research. |
| keywords:composite quantile regression streaming data online updating estimating equation |
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