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Statistical learning theory for fitting multimodal distribution to rainfall data: an application

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The promising methodology of the “Statistical Learning Theory” for the estimation of multimodal distribution is thoroughly studied. The “tail” is estimated through Hill's, UH and moment methods. The threshold value is determined by nonparametric bootstrap and the minimum mean square error criterion. Further, the “body” is estimated by the nonparametric structural risk minimization method of the empirical distribution function under the regression set-up. As an illustration, rainfall data for the meteorological subdivision of Orissa, India during the period 1871–2006 are used. It is shown that Hill's method has performed the best for tail density. Finally, the combined estimated “body” and “tail” of the multimodal distribution is shown to capture the multimodality present in the data.
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Keywords: bootstrap technique; extreme value; multimodal rainfall distribution; statistical learning theory; structural risk minimization principle

Document Type: Research Article

Affiliations: Indian Agricultural Statistics Research Institute, New Delhi110012, India

Publication date: November 1, 2011

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