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On construction of prediction intervals for heteroscedastic regression
Wu, Yun1,2; Xiong, Shifeng2
2022-06-23
Source PublicationCOMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
ISSN0361-0918
Pages26
AbstractIn a real-world regression problem, the variance usually varies with the location, which brings difficulties in prediction of the response. Existing heteroscedastic regression methods suffer from inaccurate estimation and/or high computational complexity, and cannot be used to construct satisfactory prediction intervals for small or large data. This article focuses on the problem of constructing prediction intervals of the response under heteroscedastic regression models. We apply the reconstruction approach to parameterize the mean and variance functions as forms of kernel interpolation, and use the maximum likelihood estimation method to estimate the parameters. Our method allows a small number of parameters in the parameterization forms, and only requires low computational cost. Based on these estimators and some Bayesian modifications, we provide three classes of prediction intervals. Numerical experiments for both small and large datasets indicate that they are very competitive compared with existing heteroscedastic regression methods in terms of coverage rate and computational complexity.
KeywordInterpolation Kernel method Large data Reconstruction parameterization
DOI10.1080/03610918.2022.2093370
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[12171462]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000819541800001
PublisherTAYLOR & FRANCIS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/61230
Collection中国科学院数学与系统科学研究院
Corresponding AuthorXiong, Shifeng
Affiliation1.Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, KLSC, NCMIS, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wu, Yun,Xiong, Shifeng. On construction of prediction intervals for heteroscedastic regression[J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION,2022:26.
APA Wu, Yun,&Xiong, Shifeng.(2022).On construction of prediction intervals for heteroscedastic regression.COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION,26.
MLA Wu, Yun,et al."On construction of prediction intervals for heteroscedastic regression".COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION (2022):26.
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