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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- Richardson, James W. & Klose, Steven L. & Gray, Allan W., 2000. "An Applied Procedure For Estimating And Simulating Multivariate Empirical (Mve) Probability Distributions In Farm-Level Risk Assessment And Policy Analysis," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 32(02), August.
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- Richardson, James W. & Klose, Steven L. & Gray, Allan W., 2000. "An Applied Procedure for Estimating and Simulating Multivariate Empirical (MVE) Probability Distributions In Farm-Level Risk Assessment and Policy Analysis," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 32(02), pages 299-315, August.
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