generalized mean
2.The Proof of the Generalized Cauchy Mean-value Theorem with Method of Interpolation
4.The popular TDE algorithms mainly include the classical generalized cross correlation(GCC),the adaptive least mean square(LMS),the subspace based eigenvalue decomposition(EVD)and the acoustic transfer functions ratio(ATF-s ratio) methods,etc.
5.The relationships between the intrinsic arid the extrinsic invariants of submanifolds in generalized complex space forms are studied, and the inequalities of the mean curvature and an intrinsic invariant are obtained.
6.Abstract: The generalized shrunken prediction of finite population is introduced,using generalized shrunken least squares estimator of linear regression models.With respect to prediction mean squared error,a necessary and sufficient condition for superiority of a generalized shrunken prediction over the best linear unbiased prediction is obtained.In the case of linear combination of every unit index,a linear restricting prediction is introduced and then a necessary and sufficient condition for superiority of linear restricting prediction over the best linear unbiased prediction is devived.
7.We develop two local quasi-likelihood imputation estimators for mean in a generalized varying-ciefticient model when response variables are missing at random.
8.A generalized estimator of the regression parameters is proposed in multivariate linear model. It is proved that the mean square error of ?β *(K)? is less than the square error of the least square estimator when the design matrix is ill-conditioned. It is also proved that ?β *(K)? is an admissible estimator.
9.The locally gauge invariant mean field approach is generalized from Abelian gauge groups to non-Abelian gauge groups. The SU(2) Yang-Mills-Higgs field coupling system is analyzed.
10.In the presence of impulsive noise, the two received signals are combinated, so the estimated impulse response of the channel is the eigenvector of its covariation matrix corresponding to the smallest eigenvalue, which can be realized adaptively using generalization of the normalized least mean ρ-norm (generalized NLMP) algorithm.
