Year: 1994
Journal of Computational Mathematics, Vol. 12 (1994), Iss. 4 : pp. 330–338
Abstract
The nonparametric kernel estimation of probability density function (PDF) provides a uniform and accurate estimate of flood frequency-magnitude relationship. However, the kernel estimate has the disadvantage that the smoothing factor $h$ is estimate empirically and is not locally adjusted, thus possibly resulting in deterioration of density estimate when PDF is not smooth and is heavy-tailed. Such a problem can be alleviated by estimating the density of a transformed random variable, and then taking the inverse transform. A new and efficient circular transform is proposed and investigated in this paper.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/1994-JCM-10215
Journal of Computational Mathematics, Vol. 12 (1994), Iss. 4 : pp. 330–338
Published online: 1994-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 9