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Showing 3 results for Linex Loss Function
Reza Alizadeh Noughabi, Jafar Ahmadi,
Volume 6, Issue 2 (2-2013)
Abstract
In some practical problems, obtaining observations for the variable of interest is costly and time consuming. In such situations, considering appropriate sampling schemes, in order to reduce the cost and increase the efficiency are worthwhile. In these cases, ranked set sampling is a suitable alternative for simple random sampling. In this paper, the problem of Bayes estimation of the parameter of Pareto distribution under squared error and LINEX loss functions is studied. Using a Monte Carlo simulation, for both sampling methods, namely, simple random sampling and ranked set sampling, the Bayes risk estimators are computed and compared. Finally, the efficiency of the obtained estimators is illustrated throughout using a real data set. The results demonstrate the superiority of the ranked set sampling scheme, therefore, we recommend using ranked set sampling method whenever possible.
Ali Shadrokh, Shahram Yaghoobzadeh Shahrastani,
Volume 13, Issue 2 (2-2020)
Abstract
In this study, the E-Bayesian and hierarchical Bayesian for stress-strength, when X and Y are two independent Rayleigh distributions with different parameters were estimated based on the LINEX loss function. These methods were compared with each other and with the Bayesian estimator using Monte Carlo simulation and two real data sets.
Mehran Naghizadeh Qomi,
Volume 14, Issue 2 (2-2021)
Abstract
In classical statistics, the parameter of interest is estimated based on sample information and using natural estimators such as maximum likelihood estimators. In Bayesian statistics, the Bayesian estimators are constructed based on prior knowledge and combining with it sample information. But, in some situations, the researcher has information about the unknown parameter as a guess. Bayesian shrinkage estimators can be constructed by Combining this non-sample information with sample information together with the prior knowledge, which is in the area of semi-classical statistics. In this paper, we introduce a class of Bayesian shrinkage estimators for the Weibull scale parameter as a generalization of the estimator at hand and consider the bias and risk of them under LINEX loss function. Then, the proposed estimators are compared using a real data set.
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