Simulating Multivariate Distributions with Sparse Data: A Kernal Density Smoothing Procedure
Often analysts must conduct risk analysis based on a small number of observations. This paper describes and illustrates the use of a kernel density estimation procedure to smooth out irregularities in such a sparse data set for simulating univariate and multivariate probability distributions.
|Date of creation:||2006|
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- repec:ags:joaaec:v:32:y:2000:i:2:p:299-315 is not listed on IDEAS
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