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Showing 2 results for Squared Error Loss Function
Ahad Malekzadeh, Mina Tohidi,
Volume 4, Issue 2 (3-2011)
Abstract
Coefficient of determination is an important criterion in different applications. The problem of point estimation of this parameter has been considered by many researchers. In this paper, the class of linear estimators of R^2 was considered. Then, two new estimators were proposed, which have lower risks than other usual estimator, such as the sample coefficient of determination and its adjusted form. Also on the basis of some simulations, we show that the Jacknife estimator is an efficient estimator with lower risk, when the number of observations is small.
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.
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