Heterogeneous Learning of Functional Clustering Regression and Application to Chinese Air Pollution Data

Tingting Wang, Linjie Qin (), Chao Dai, Zhen Wang and Chenqi Gong
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Tingting Wang: School of Statistics, Huaqiao University, Xiamen 361021, China
Linjie Qin: Department of Economics, Xiamen University, Xiamen 361005, China
Chao Dai: School of Statistics, Huaqiao University, Xiamen 361021, China
Zhen Wang: School of Statistics, Huaqiao University, Xiamen 361021, China
Chenqi Gong: College of Mathematics and Statistics, Chongqing University, Chongqing 401331, China

IJERPH, 2023, vol. 20, issue 5, 1-21

Abstract: Clustering algorithms are widely used to mine the heterogeneity between meteorological observations. However, traditional applications suffer from information loss due to data processing and pay little attention to the interaction between meteorological indicators. In this paper, we combine the ideas of functional data analysis and clustering regression, and propose a functional clustering regression heterogeneity learning model (FCR-HL), which respects the data generation process of meteorological data while incorporating the interaction between meteorological indicators into the analysis of meteorological data heterogeneity. In addition, we provide an algorithm for FCR-HL to automatically select the number of clusters, which has good statistical properties. In the later empirical study based on PM 2.5 concentrations and PM 10 concentrations in China, we found that the interaction between PM 10 and PM 2.5 varies significantly between regions, showing several types of significant patterns, which provide meteorologists with new perspectives to further study the effects between meteorological indicators.

Keywords: functional data analysis; heterogeneity learning; clustering regression; meteorological data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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