In this paper, the authors generalize the definition of χ 2 distribution and introduce a quasi χ 2 distribution, and then prove several properties of it, find the necessary and sufficient conditions of i...In this paper, the authors generalize the definition of χ 2 distribution and introduce a quasi χ 2 distribution, and then prove several properties of it, find the necessary and sufficient conditions of independence about multivariate normal distributions, matrix normal distributions and two parts of the Wishart distribution.展开更多
We consider an Error-in-Variable partially linear model where the covariates of linear part are measured with error which follows a normal distribution with a known covariance matrix. We propose a corrected-loss estim...We consider an Error-in-Variable partially linear model where the covariates of linear part are measured with error which follows a normal distribution with a known covariance matrix. We propose a corrected-loss estimation of the covariate effect. The proposed estimator is asymptotically normal. Simulation studies are presented to show that the proposed method performs well with finite samples, and the proposed method is applied to a real data set.展开更多
文摘In this paper, the authors generalize the definition of χ 2 distribution and introduce a quasi χ 2 distribution, and then prove several properties of it, find the necessary and sufficient conditions of independence about multivariate normal distributions, matrix normal distributions and two parts of the Wishart distribution.
基金supported by National Natural Science Foundation of China(Grant Nos.10901020 and 11371062)the Fundamental Research Funds for the Central Universities,Beijing Center for Mathematics and Information Interdisciplinary Sciences,China Zhongdian Project(Grant No.11131002)
文摘We consider an Error-in-Variable partially linear model where the covariates of linear part are measured with error which follows a normal distribution with a known covariance matrix. We propose a corrected-loss estimation of the covariate effect. The proposed estimator is asymptotically normal. Simulation studies are presented to show that the proposed method performs well with finite samples, and the proposed method is applied to a real data set.
