The matrix equation AXB = E with the constraint PX = sXP is considered, where P is a given Hermitian matrix satisfying P^2 = I and s = ±1. By an eigenvalue decomposition of P, the constrained problem can be equiv...The matrix equation AXB = E with the constraint PX = sXP is considered, where P is a given Hermitian matrix satisfying P^2 = I and s = ±1. By an eigenvalue decomposition of P, the constrained problem can be equivalently transformed to a well-known unconstrained problem of matrix equation whose coefficient matrices contain the corresponding eigenvector, and hence the constrained problem can be solved in terms of the eigenvectors of P. A simple and eigenvector-free formula of the general solutions to the constrained problem by generalized inverses of the coefficient matrices A and B is presented. Moreover, a similar problem of the matrix equation with generalized constraint is discussed.展开更多
This paper considers the convergence rates for nonparametric estimators of the error distribution in semi-parametric regression models. By establishing some general laws of the iterated logarithm, it shows that the ra...This paper considers the convergence rates for nonparametric estimators of the error distribution in semi-parametric regression models. By establishing some general laws of the iterated logarithm, it shows that the rates of convergence of either the empirical distribution or a smoothed version of the empirical distribution function matches exactly the rates obtained for an independent sample from the error distribution.展开更多
基金The work of the first author was supported by the Young Talent Foundation of Zhejiang Gongshang University
文摘The matrix equation AXB = E with the constraint PX = sXP is considered, where P is a given Hermitian matrix satisfying P^2 = I and s = ±1. By an eigenvalue decomposition of P, the constrained problem can be equivalently transformed to a well-known unconstrained problem of matrix equation whose coefficient matrices contain the corresponding eigenvector, and hence the constrained problem can be solved in terms of the eigenvectors of P. A simple and eigenvector-free formula of the general solutions to the constrained problem by generalized inverses of the coefficient matrices A and B is presented. Moreover, a similar problem of the matrix equation with generalized constraint is discussed.
基金supported by the National Science Foundation of China under Grant Nos.11201422,11301481,and 11371321Zhejiang Provincial Natural Science Foundation of China under Grant Nos.Y6110639,Y6110110,LQ12A01018,and LQ12A01017+2 种基金the National Statistical Science Research Project of China under Grant No.2012LY174Foundation for Young Talents of ZJGSU under Grant No.1020XJ1314019Zhejiang Provincial Key Research Base for Humanities and Social Science Research(Statistics)
文摘This paper considers the convergence rates for nonparametric estimators of the error distribution in semi-parametric regression models. By establishing some general laws of the iterated logarithm, it shows that the rates of convergence of either the empirical distribution or a smoothed version of the empirical distribution function matches exactly the rates obtained for an independent sample from the error distribution.
