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Model-averaging-based semiparametric modeling for conditional quantile prediction.

Model-averaging-based semiparametric modeling for conditional quantile prediction.

Authors :
Guo, Chaohui
Zhang, Wenyang
Source :
SCIENCE CHINA Mathematics; Dec2024, Vol. 67 Issue 12, p2843-2872, 30p
Publication Year :
2024

Abstract

In real data analysis, the underlying model is frequently unknown. Hence, the modeling strategy plays a key role in the success of data analysis. Inspired by the idea of model averaging, we propose a novel semiparametric modeling strategy for the conditional quantile prediction, without assuming that the underlying model is any specific parametric or semiparametric model. Due to the optimality of the weights selected by leave-one-out cross-validation, the proposed modeling strategy provides a more precise prediction than those based on some commonly used semiparametric models such as the varying coefficient and additive models. Asymptotic properties are established in the proposed modeling strategy along with its estimation procedure. We conducted extensive simulations to compare our method with alternatives across various scenarios. The results show that our method provides more accurate predictions. Finally, we applied our approach to the Boston housing data, yielding more precise quantile predictions of house prices compared with commonly used methods, and thus offering a clearer picture of the Boston housing market. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16747283
Volume :
67
Issue :
12
Database :
Complementary Index
Journal :
SCIENCE CHINA Mathematics
Publication Type :
Academic Journal
Accession number :
181066484
Full Text :
https://doi.org/10.1007/s11425-022-2205-1