In this paper,the empirical likelihood confidence regions for the regression coefficient in a linear model are constructed under m-dependent errors.It is shown that the blockwise empirical likelihood is a good way to ...In this paper,the empirical likelihood confidence regions for the regression coefficient in a linear model are constructed under m-dependent errors.It is shown that the blockwise empirical likelihood is a good way to deal with dependent samples.展开更多
In this paper,we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process.A two-step procedure is proposed for estimating the unknown parameters by taking ...In this paper,we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process.A two-step procedure is proposed for estimating the unknown parameters by taking into account of the special structure in error.Since the asymptotic matrix of the estimator for the parametric part has a complex structure,an empirical likelihood function is also developed.We derive the asymptotic properties of the related statistics under mild conditions.Some simulations,as well as a real data example,are conducted to illustrate the finite sample performance.展开更多
文摘In this paper,the empirical likelihood confidence regions for the regression coefficient in a linear model are constructed under m-dependent errors.It is shown that the blockwise empirical likelihood is a good way to deal with dependent samples.
基金This research is supported by the National Social Science Foundation of China under Grant No. 11CTJ004, the National Natural Science Foundation of China under Grant Nos. 10871013 and 10871217, the National Natural Science Foundation of Beijing under Grant No. 1102008, the Research Foundation of Chongqing Municipal Education Commission under Grant Nos. KJ110720 and KJ100726, and the Natural Science Foundation of Guangxi under Grant No. 2010GXNSFB013051.
基金supported by the National Natural Science Foundation of China under Grant Nos.11271088and 11361011the Natural Science Foundation of Guangxi under Grant Nos.2013GXNSFAA019004 and2013GXNSFAA019007
文摘这份报纸建议使用 blockwise 在否定地联系的错误下面在一个部分线性的模型为回归向量构造信心区域的实验可能性的(EL ) 方法。这被显示出 blockwise EL 比率统计数值为 asymptotically， <sup>2</sup> 散布了。结果被用来获得一个基于 EL 的信心区域为。建议信心区域的有限样品表演上的模拟研究的结果被报导。
基金supported by the NSF of China(Nos.11971208,11601197)the NSSF of China(Grant No.21&ZD152)+2 种基金the China Postdoctoral Science Foundation(Nos.2016M600511,2017T100475)the NSF of Jiangxi Province(Nos.2018ACB21002,20171ACB21030)the Post graduate Innovation Project of Jiangxi Province(No.YC2021CB124)。
文摘In this paper,we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process.A two-step procedure is proposed for estimating the unknown parameters by taking into account of the special structure in error.Since the asymptotic matrix of the estimator for the parametric part has a complex structure,an empirical likelihood function is also developed.We derive the asymptotic properties of the related statistics under mild conditions.Some simulations,as well as a real data example,are conducted to illustrate the finite sample performance.
基金supported by National Natural Science Foundation of China(10971081,J0630104)Program for New Century Excellent Talents in University(NCET-08-237)+2 种基金Scientific Research Fund of Jilin University (200810024)Natural Science Foundation of Jilin Provincial Science & Technology Department(20101599, 201015100)985 project of Jilin University and the Doctor Foundation at Jilin University of Finace and Economics (10XB20)
基金Supported by the National Natural Science Foundation of Guangxi(2016GXNS FAA3800163)the Basic Ability Promotion Project for Young and Middle-age Teachers of Guangxi(KY2016YB862)the Professor Nurturing Engineering Project of Nanning University(2016JSGC09)
