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...

• In the present paper we study the performance of the Bayes Shrinkage estimators for the scale parameter of the Weibull distribution under the squared error loss and the LINEX loss functions in the presence of a prior point information of the scale parameter when Type-II censored data are available. The properties of the minimax estimators are also discussed. Key-Words: • Bayes shrinkage estim...

In this paper, given a progressively type II censored sample from a generalized half logistic distribution, the Bayesian estimators are obtained under asymmetric loss functions such as LINEX loss function, Precautionary loss function, Entropy loss function for the parameter, Reliability and cumulative hazard functions. Comparisons of these estimators are made through simulation studies. Mathema...

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...

Bayes and classical estimators have been obtained for two-parameter exponentiated-Weibull distribution when sample is available from type-II censoring scheme. Bayes estimators have been developed under squared error loss function as well as under LINEX loss function using non-informative type of priors for the parameters. Besides, the generalized maximum likelihood estimators and the usual maxi...

It is now well recognized that the use of the squared error loss function (SELF) in Bayesian estimation may not be appropriate when positive and negative errors have different consequences. To overcome this difficulty, Varian (1975) and Zellner (1986) proposed an asymmetric loss function known as the LINEX loss function (LLF) and its invariant form (Basu & Ebrahimi, 1991) for any parameter θ is...

The risk of the family of feasible generalized double k-class estimators under LINEX loss function is derived in a linear regression model. The disturbances are assumed to be non-spherical and their variance covariance matrix is unknown.