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

estimation of the multivariate normal mean under the extended reflected normal loss function

Estimating a Bounded Normal Mean Under the LINEX Loss Function

Let X be a random variable from a normal distribution with unknown mean θ and known variance σ2. In many practical situations, θ is known in advance to lie in an interval, say [−m,m], for some m > 0. As the usual estimator of θ, i.e., X under the LINEX loss function is inadmissible, finding some competitors for X becomes worthwhile. The only study in the literature considered the problem of min...

Simultaneous Estimation of the Multivariate Normal Mean under Balanced Loss Function

This paper considers simultaneous estimation of multivariate normal mean vector using Zellner's(1994) balanced loss function which is deened as follows :

estimating a bounded normal mean under the linex loss function

let x be a random variable from a normal distribution with unknown mean θ and known variance σ2. in many practical situations, θ is known in advance to lie in an interval, say [−m,m], for some m > 0. as the usual estimator of θ, i.e., x under the linex loss function is inadmissible, finding some competitors for x becomes worthwhile. the only study in the literature considered the problem of min...

Admissibility of Linear Predictors of Finite Population Parameters under Reflected Normal Loss Function

One of the most important prediction problems in finite population is the prediction of a linear function of characteristic values of a finite population. In this paper the admissibility of linear predictors of an arbitrary linear function of characteristic values in a finite population under reflected normal loss function is considered. Under the super-population model, we obtain the condition...

ESTIMATION OF SCALE PARAMETER UNDER A REFLECTED GAMMA LOSS FUNCTION

In this paper, the estimation of a scale parameter t under a new and bounded loss function, based on a reflection of the gamma density function, is discussed. The best scale-invariant estimator of tis obtained and the admissibility of all linear functions of the sufficient statistic, for estimating t in the absence of a nuisance parameter, is investigated