Post-J test inference in non-nested linear regression models

CSpace

	Post-J test inference in non-nested linear regression models
其他题名	Post-J test inference in non-nested linear regression models
	Chen XinJie 1; Fan YanQin 2; Wan Alan 3; Zou GuoHua 4
	2015
发表期刊	SCIENCE CHINA-MATHEMATICS
ISSN	1674-7283
卷号	58 期号:6 页码:1203-1216
摘要	This paper considers the post-J test inference in non-nested linear regression models. Post-J test inference means that the inference problem is considered by taking the first stage J test into account. We first propose a post-J test estimator and derive its asymptotic distribution. We then consider the test problem of the unknown parameters, and a Wald statistic based on the post-J test estimator is proposed. A simulation study shows that the proposed Wald statistic works perfectly as well as the two-stage test from the view of the empirical size and power in large-sample cases, and when the sample size is small, it is even better. As a result, the new Wald statistic can be used directly to test the hypotheses on the unknown parameters in non-nested linear regression models.
其他摘要	This paper considers the post-J test inference in non-nested linear regression models. Post-J test inference means that the inference problem is considered by taking the first stage J test into account. We first propose a post-J test estimator and derive its asymptotic distribution. We then consider the test problem of the unknown parameters, and a Wald statistic based on the post-J test estimator is proposed. A simulation study shows that the proposed Wald statistic works perfectly as well as the two-stage test from the view of the empirical size and power in large-sample cases, and when the sample size is small, it is even better. As a result, the new Wald statistic can be used directly to test the hypotheses on the unknown parameters in non-nested linear regression models.
关键词	DISTRIBUTIONS COEFFICIENTS VARIANCES EQUALITY non-nested linear regression post-J test Wald statistic
收录类别	CSCD
语种	英语
资助项目	[General Research Fund from the Hong Kong Research Grants Council] ; [National Natural Science Foundation of China] ; [Hundred Talents Program of the Chinese Academy of Sciences]
CSCD记录号	CSCD:5444904
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/56110
专题	中国科学院数学与系统科学研究院
作者单位	1.中国科学院数学与系统科学研究院 2.范德堡大学 3.伦敦城市大学 4.首都师范大学
推荐引用方式 GB/T 7714	Chen XinJie,Fan YanQin,Wan Alan,et al. Post-J test inference in non-nested linear regression models[J]. SCIENCE CHINA-MATHEMATICS,2015,58(6):1203-1216.
APA	Chen XinJie,Fan YanQin,Wan Alan,&Zou GuoHua.(2015).Post-J test inference in non-nested linear regression models.SCIENCE CHINA-MATHEMATICS,58(6),1203-1216.
MLA	Chen XinJie,et al."Post-J test inference in non-nested linear regression models".SCIENCE CHINA-MATHEMATICS 58.6(2015):1203-1216.