With the upper bound of Kullback-Leibler distance between a matrix variate Beta-distri- bution and a normal distribution, this paper gives the conditions under which a matrix-variate Beta- distribution will approach u...With the upper bound of Kullback-Leibler distance between a matrix variate Beta-distri- bution and a normal distribution, this paper gives the conditions under which a matrix-variate Beta- distribution will approach uniformly and asymptotically a normal distribution.展开更多
In this paper, the parameters of a p-dimensional linear structural EV(error-in-variable)model are estimated when the coefficients vary with a real variable and the model error is time series.The adjust weighted least ...In this paper, the parameters of a p-dimensional linear structural EV(error-in-variable)model are estimated when the coefficients vary with a real variable and the model error is time series.The adjust weighted least squares(AWLS) method is used to estimate the parameters. It is shown that the estimators are weakly consistent and asymptotically normal, and the optimal convergence rate is also obtained. Simulations study are undertaken to illustrate our AWLSEs have good performance.展开更多
基金Supported by the Educational Commission of Hubei Province of China(Grant No.D20112503)National Natural Science Foundation of China(Grant No.11071022)
文摘With the upper bound of Kullback-Leibler distance between a matrix variate Beta-distri- bution and a normal distribution, this paper gives the conditions under which a matrix-variate Beta- distribution will approach uniformly and asymptotically a normal distribution.
基金Supported by the Educational Commission of Hubei Province of China(Grant No.D20112503)National Natural Science Foundation of China(Grant Nos.11071022,11231010 and 11028103)the foundation of Beijing Center of Mathematics and Information Sciences
文摘In this paper, the parameters of a p-dimensional linear structural EV(error-in-variable)model are estimated when the coefficients vary with a real variable and the model error is time series.The adjust weighted least squares(AWLS) method is used to estimate the parameters. It is shown that the estimators are weakly consistent and asymptotically normal, and the optimal convergence rate is also obtained. Simulations study are undertaken to illustrate our AWLSEs have good performance.
