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Nonparametric variable kernel estimation with historical floods and paleoflood information
Author(s) -
Guo Sheng Lian
Publication year - 1991
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/90wr01972
Subject(s) - nonparametric statistics , kernel density estimation , quantile , econometrics , smoothing , parametric statistics , variable (mathematics) , kernel (algebra) , kernel smoother , statistics , estimation , computer science , mathematics , kernel method , artificial intelligence , engineering , mathematical analysis , combinatorics , estimator , systems engineering , radial basis function kernel , support vector machine
Extending a data record back in time using historical or paleoflood data has the potential to provide a considerable amount of information on very large floods. Parametric estimation methods are readily applicable to flood frequency analysis when historical or paleoflood estimates are available. However, all parametric approaches need an assumption about the underlying parent distribution, which is never known exactly. In recent years nonparametric methods of estimating probability density functions have been developed. Each of these involves the use of a suitable smoothing function known as a kernel. A new nonparametric variable kernel estimation model is proposed. Results obtained from a limited amount of real data and from simulation experiments show that quantiles estimated by the nonparametric method are better than those estimated by some selected parametric models both in descriptive ability and in predictive ability. The uncertainty in the choice of the threshold value of perception of a historical flood is also discussed.