EVALUATION OF KERNEL DENSITY-ESTIMATION METHODS FOR DAILY PRECIPITATION RESAMPLING

Kernel density estimators are useful building blocks for empirical sta tistical 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) prov ide a useful basis for Monte Carlo resampling and are also useful for posing and testing hypotheses (e.g. bimodality) as to the frequency di stributions of the variable. In this paper, some issues related to the selection and design of univariate kernel density estimators are revi ewed. 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 resam pling methodology presented in Lall et al. (1996).