@Article{JCM-12-330,
author = {Kaz Adamowski and Wojciech Feluch},
title = {The Transformed Nonparametric Flood Frequency Analysis},
journal = {Journal of Computational Mathematics},
year = {1994},
volume = {12},
number = {4},
pages = {330--338},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {1991-7139},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jcm/10215.html}
}