Statistical Diagnosis Based on Quantile Regression for A Distributed System
With the arrival of the era of big data,distributed systems have been widely used in our lives.However,due to the unlimited number of servers in the distributed system,the heterogeneity between the various servers is high,which may affect the results of statis-tical inference.Therefore,statistical diagnosis in distributed systems becomes very necessary and important.For this reason,an outlier detection method which is suitable for quantile regression model in a distributed system is proposed.Considering the practical applica-tion background,the method of group(subset in distributed system)deletion is used to capture the impact of marginal correlation,and make statistical diagnosis in a more robust model.In the Monte Carlo simulation study,the method performs well,and its effectiveness is further verified by the detection of the actual data of the air quality monitoring station.
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