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Comments on “Plotting Positions in Extreme Value Analysis”

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  • 1 RWDI Anemos, Ltd., Dunstable, United Kingdom
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Abstract

This comment addresses the role of sampling error in extreme value analysis. A note published in this journal claimed that estimator for sample probability has a unique status that invalidates all other estimators and renders invalid all of the developments of unbiased distribution-dependent estimators made since 1939. The note concluded that the use of distribution-dependent estimators should be abandoned and that many estimates of the weather-related risks should be reevaluated and the related building codes and other related regulations updated. This comment uses rigorous statistical proofs to make the diametrically opposite case: namely, that development of distribution-dependent estimators has resulted in an improvement in accuracy over the past half century and that no changes are required to the basis of weather-related building codes and regulations. These rigorous proofs are supplemented by sampling experiments that demonstrate their validity. This comment provides an introduction to the basic statistical concepts of the statistical modeling of extremes, including unbiased estimators for the model parameters.

Corresponding author address: Nicholas Cook, RWDI Anemos, Ltd., Unit 4, Lawrence Way, Dunstable, Bedfordshire LU6 1BD, United Kingdom. Email: njcook@ntlworld.com

Abstract

This comment addresses the role of sampling error in extreme value analysis. A note published in this journal claimed that estimator for sample probability has a unique status that invalidates all other estimators and renders invalid all of the developments of unbiased distribution-dependent estimators made since 1939. The note concluded that the use of distribution-dependent estimators should be abandoned and that many estimates of the weather-related risks should be reevaluated and the related building codes and other related regulations updated. This comment uses rigorous statistical proofs to make the diametrically opposite case: namely, that development of distribution-dependent estimators has resulted in an improvement in accuracy over the past half century and that no changes are required to the basis of weather-related building codes and regulations. These rigorous proofs are supplemented by sampling experiments that demonstrate their validity. This comment provides an introduction to the basic statistical concepts of the statistical modeling of extremes, including unbiased estimators for the model parameters.

Corresponding author address: Nicholas Cook, RWDI Anemos, Ltd., Unit 4, Lawrence Way, Dunstable, Bedfordshire LU6 1BD, United Kingdom. Email: njcook@ntlworld.com

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