Identifying interaction effects via additive quantile regression models

doi:10.4310/SII.2017.v10.n2.a9

	Identifying interaction effects via additive quantile regression models
	Zhu, Qianqian 1,2; Hu, Yanan 3; Tian, Maozai1,4,5
	2017
发表期刊	Statistics and Its Interface
卷号	10 期号:2 页码:255-265
摘要	As additive quantile regression (AQR) models possess the properties of robustness and flexibility, they become increasingly popular in many applications. However, such models may fail when predictors reflect interaction effects in the response. In fact, we often encounter such a problem that the main effects are not significant but the pairwise interactions are in regression. The existence of such a situation is neither accidental nor ignorable. Overlooking the interaction effects may render many of the traditional statistical techniques used for studying data relationships invalid. In these situations, it is necessary to consider more reasonable models such as AQR model with pairwise interactions. This paper mainly studies estimation and testing for the AQR model with pairwise interactions. To estimate the unknown functions in the model, local linear fitting and ordinary backfitting methods are applied. The generalized likelihood ratio (GLR) type test statistic is constructed to test the overall significance of pairwise interactions, and bootstrap method is utilized to approximate the asymptotic distribution of the test statistic. Theoretical properties of estimators and GLR type test statistic are derived. Bandwidth selection based on plug-in method for pairwise interactions is discussed as well. Finally, simulation study and a simple empirical analysis are presented to illustrate the performance of the proposed model.
关键词	Additive quantile models Back fitting algorithm Bandwidth selection Generalized likelihood ratio type testing Pairwise interaction
DOI	10.4310/SII.2017.v10.n2.a9
收录类别	SCIE
ISSN	1938-7989
语种	英语
WOS研究方向	Mathematical & Computational Biology ; Mathematics
WOS类目	Mathematical & Computational Biology ; Mathematics, Interdisciplinary Applications
WOS记录号	WOS:000389015500009
出版者	INT PRESS BOSTON, INC
原始文献类型	Article
EISSN	1938-7997
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/34544
专题	统计与数据科学学院
作者单位	1.Renmin Univ China, Sch Stat, Ctr Appl Stat, Beijing 100872, Peoples R China; 2.Univ Hong Kong, Dept Stat & Actuarial Sci, Hong Kong, Hong Kong, Peoples R China; 3.Renmin Univ China, Sch Stat, Beijing 100872, Peoples R China; 4.Lanzhou Univ Finance & Econ, Sch Stat, Lanzhou 730101, Gansu, Peoples R China; 5.Xinjiang Univ Finance & Econ, Sch Stat & Informat, Urumqi 830012, Xinjiang, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Qianqian,Hu, Yanan,Tian, Maozai. Identifying interaction effects via additive quantile regression models[J]. Statistics and Its Interface,2017,10(2):255-265.
APA	Zhu, Qianqian,Hu, Yanan,&Tian, Maozai.(2017).Identifying interaction effects via additive quantile regression models.Statistics and Its Interface,10(2),255-265.
MLA	Zhu, Qianqian,et al."Identifying interaction effects via additive quantile regression models".Statistics and Its Interface 10.2(2017):255-265.