An algorithm for distributed parameter estimation in modal regression models

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 compu...

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Bibliographic Details
Published inStatistical theory and related fields Vol. 9; no. 2; pp. 101 - 123
Main Authors Ma, Xuejun, Xia, Xiaochao
Format Journal Article
LanguageEnglish
Published Taylor & Francis Group 03.04.2025
Subjects
divide-and-conquer
linear approximation
massive data
Modal regression
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Summary: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.
ISSN:2475-4269
2475-4277
DOI:10.1080/24754269.2025.2483553