Estimating the Common Mean of k Normal Populations with Known Variance

ESTIMATING THE MEAN OF INVERSE GAUSSIAN DISTRIB WTION WITH KNOWN COEFFICIENT OF VARIATION UNDER ENTROPY LOSS

An estimation problem of the mean µ of an inverse Gaussian distribution IG(µ, C µ) with known coefficient of variation c is treated as a decision problem with entropy loss function. A class of Bayes estimators is constructed, and shown to include MRSE estimator as its closure. Two important members of this class can easily be computed using continued fractions

Estimating Variance of the Sample Mean in Two-phase Sampling with Unit Non-response Effect

In sample surveys, we always deal with two types of errors: Sampling error and non-sampling error. One of the most common non-sampling errors is nonresponse. This error happens when some sample units are not observed or viewed but they do not answer some of the questions. The complete prevention of this error is not possible, but it can be significantly reduced. The non-response causes bias and ...

Mixture of Normal Mean-Variance of Lindley Distributions

‎Abstract: In this paper, a new mixture modelling using the normal mean-variance mixture of Lindley (NMVL) distribution has been considered. The proposed model is heavy-tailed and multimodal and can be used in dealing with asymmetric data in various theoretic and applied problems. We present a feasible computationally analytical EM algorithm for computing the maximum likelihood estimates. T...

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...

Ad-757 094 Sequential Estimation of the Largest Normal Mean When the Variance Is Known