@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} }