Evaluation of Kernel Density Estimation Methods for Daily Precipitation Resampling

Document Type

Article

Journal/Book Title/Conference

Stochastic Hydrology and Hydraulics

Volume

11

Issue

6

Publisher

Springer-Verlag

Publication Date

12-1-1997

First Page

523

Last Page

547

Abstract

Kernel density estimators are useful building blocks for empirical statistical modeling of precipitation and other hydroclimatic variables. Data driven estimates of the marginal probability density function of these variables (which may have discrete or continuous arguments) provide a useful basis for Monte Carlo resampling and are also useful for posing and testing hypotheses (e.g bimodality) as to the frequency distributions of the variable. In this paper, some issues related to the selection and design of univariate kernel density estimators are reviewed. Some strategies for bandwidth and kernel selection are discussed in an applied context and recommendations for parameter selection are offered. This paper complements the nonparametric wet/dry spell resampling methodology presented in Lall et al. (1996).

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