TY - JOUR
T1 - The Transformed Nonparametric Flood Frequency Analysis
AU - Kaz Adamowski & Wojciech Feluch
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 - <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>