In this paper,we propose a new algorithm to handle massive data sets,which are modelled by modal regression models.Differing from the existing methods regarding distributed modal regression,the proposed method combine...In this paper,we propose a new algorithm to handle massive data sets,which are modelled by modal regression models.Differing from the existing methods regarding distributed modal regression,the proposed method combines the divide-and-conquer idea and a linear approximation algorithm.It is computationally fast and statistically efficient to implement.Theoretical analysis for the resultant distributed estimator under some regularity conditions is presented.Simulation studies are conducted to assess the effectiveness and flexibility of the proposed method with a finite sample size.Finally,an empirical application to the chemical sensors data is analysed for further illustration.展开更多
基金supported by the National Natural Science Foundation of China(grant number 12101439)supported by Fundamental Research Funds for the Central Universities[grant number 2021CDJQY-047]National Natural Science Foundation of China[grant number 11801202].
文摘In this paper,we propose a new algorithm to handle massive data sets,which are modelled by modal regression models.Differing from the existing methods regarding distributed modal regression,the proposed method combines the divide-and-conquer idea and a linear approximation algorithm.It is computationally fast and statistically efficient to implement.Theoretical analysis for the resultant distributed estimator under some regularity conditions is presented.Simulation studies are conducted to assess the effectiveness and flexibility of the proposed method with a finite sample size.Finally,an empirical application to the chemical sensors data is analysed for further illustration.
