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Bayesian Estimation and Prediction Using Asymmetric Loss Functions
Journal of the American Statistical Association
Vol. 81, No. 394 (Jun., 1986), pp. 446-451
Stable URL: http://www.jstor.org/stable/2289234
Page Count: 6
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Estimators and predictors that are optimal relative to Varian's asymmetric LINEX loss function are derived for a number of well-known models. Their risk functions and Bayes risks are derived and compared with those of usual estimators and predictors. It is shown that some usual estimators, for example, a scalar sample mean or a scalar least squares regression coefficient estimator, are inadmissible relative to asymmetric LINEX loss by providing alternative estimators that dominate them uniformly in terms of risk.
Journal of the American Statistical Association © 1986 American Statistical Association