Improved second order estimation in the singular multivariate normal model

Modified Laplace Decomposition Method for Singular IVPs in the second-Order Ordinary Differential Equations

  In this paper, we use modified Laplace decomposition method to solving initial value problems (IVP) of the second order ordinary differential equations. Theproposed method can be applied to linear and nonlinearproblems    

An improved second-order continuum traffic model

We construct a second-order continuum traffic model by using an iterative procedure in order to derive a constitutive relation for the traffic pressure which is similar to the Navier–Stokes equation for ordinary fluids. Our second-order traffic model represents an improvement on the traffic model suggested by Kerner and Konhäuser since the iterative procedure introduces, in the constitutive rel...

Improved minimax estimation of a multivariate normal mean under heteroscedasticity

Consider the problem of estimating a multivariate normal mean with a known variance matrix, which is not necessarily proportional to the identity matrix. The coordinates are shrunk directly in proportion to their variances in Efron and Morris’ (J. Amer. Statist. Assoc. 68 (1973) 117–130) empirical Bayes approach, whereas inversely in proportion to their variances in Berger’s (Ann. Statist. 4 (1...

Estimation of the Multivariate Normal Mean under the Extended Reflected Normal Loss Function

Estimation of the mean vector in a singular multivariate normal distribution

This paper addresses the problem of estimating the mean vector of a singular multivariate normal distribution with an unknown singular covariance matrix. The maximum likelihood estimator is shown to be minimax relative to a quadratic loss weighted by the Moore-Penrose inverse of the covariance matrix. An unbiased risk estimator relative to the weighted quadratic loss is provided for a Baranchik...