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Testing approximate normality of an estimator using the estimated MSE and bias with an application to the shape parameter of the generalized Pareto distribution

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Published Article
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arXiv ID: 1210.2253
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arXiv
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Abstract

Often it is not easy to choose between estimators, based on the estimated MSE and bias using simulation studies. Normality in small samples and a variance of the estimator, which is correct and easy to calculate using a single sample, give the added advantage that hypotheses concerning the parameter can be tested in new samples. A procedure to check normality is proposed where previously published MSE and bias are used to perform a test for normality. A confidence interval for the index of the S&P500 index is found by applying the results to estimators of the generalized Pareto distribution.

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