Renewable Composite Quantile Regression for Streaming Data Sets
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.
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