@Article{JCM-12-4,
author = {},
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/1994-JCM-10215},
url = {https://global-sci.com/article/85874/the-transformed-nonparametric-flood-frequency-analysis}
}