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Volume 12, Issue 4
The Transformed Nonparametric Flood Frequency Analysis

Kaz Adamowski & Wojciech Feluch

J. Comp. Math., 12 (1994), pp. 330-338.

Published online: 1994-12

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  • 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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@Article{JCM-12-330, author = {}, title = {The Transformed Nonparametric Flood Frequency Analysis}, journal = {Journal of Computational Mathematics}, year = {1994}, volume = {12}, number = {4}, pages = {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.

}, issn = {1991-7139}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jcm/10215.html} }
TY - JOUR T1 - The Transformed Nonparametric Flood Frequency Analysis JO - Journal of Computational Mathematics VL - 4 SP - 330 EP - 338 PY - 1994 DA - 1994/12 SN - 12 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jcm/10215.html KW - AB -

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.

Kaz Adamowski & Wojciech Feluch. (1970). The Transformed Nonparametric Flood Frequency Analysis. Journal of Computational Mathematics. 12 (4). 330-338. doi:
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