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Assessing white noise assumption with semi-parametric additive partial linear models
Zhang, Tianyong1; Yuan, Demei1; Ma, Jiali1; Hu, Xuemei1,2
2017-06-01
Source PublicationSTATISTICAL PAPERS
ISSN0932-5026
Volume58Issue:2Pages:417-431
AbstractIn this paper, we present two test statistics for assessing white noise assumption with semi-parametric additive partial linear models. The test statistics are shown to have asymptotic normal or chi-squared distributions under the null hypothesis that the model errors belong to white noise series. By applying R, Monte Carlo experiments are conducted to examine the finite sample performance of the test statistics. Simulation results indicate that the test statistics perform satisfactorily in both estimated sizes and powers.
KeywordWhite noise Semi-parametric additive partial linear models Empirical likelihood
DOI10.1007/s00362-015-0705-z
Language英语
Funding ProjectNational Natural Science Foundation of China[11101452] ; Natural Science Foundation Project of CQ CSTC[2012jjA00035] ; SCR of Chongqing Municipal Education Commission[KJ1400613] ; National Basic Research Program of China (973 Program)[2011CB808000]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000401414200007
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/25465
Collection中国科学院数学与系统科学研究院
Corresponding AuthorHu, Xuemei
Affiliation1.Chongqing Technol & Business Univ, Sch Math & Stat, Chongqing, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Tianyong,Yuan, Demei,Ma, Jiali,et al. Assessing white noise assumption with semi-parametric additive partial linear models[J]. STATISTICAL PAPERS,2017,58(2):417-431.
APA Zhang, Tianyong,Yuan, Demei,Ma, Jiali,&Hu, Xuemei.(2017).Assessing white noise assumption with semi-parametric additive partial linear models.STATISTICAL PAPERS,58(2),417-431.
MLA Zhang, Tianyong,et al."Assessing white noise assumption with semi-parametric additive partial linear models".STATISTICAL PAPERS 58.2(2017):417-431.
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