The nonparametric kernel estimation of probability density function (PDF) pro-vides a uniform and accurate estimate of flood frequency-magnitude relationship.However, the kernel estimate has the disadvantage that the ...The nonparametric kernel estimation of probability density function (PDF) pro-vides 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 deteri oration of density estimate when PDF is not smooth and is heavy-tailed. Such a problem can be alleviate by estimating the density of a transformed random vari able, and then taking the inverse transform. A new and efficient circular transform is proposed and investigated in this paper展开更多
文摘The nonparametric kernel estimation of probability density function (PDF) pro-vides 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 deteri oration of density estimate when PDF is not smooth and is heavy-tailed. Such a problem can be alleviate by estimating the density of a transformed random vari able, and then taking the inverse transform. A new and efficient circular transform is proposed and investigated in this paper
