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,"
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Southern Agricultural Economics Association, vol. 32(02), August.
- 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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- repec:ags:joaaec:v:32:y:2000:i:2:p:299-315 is not listed on IDEAS
- Hardaker, J. Brian & Lien, Gudbrand D., 2005. "Towards some principles of good practice for decision analysis in agriculture," 2005 Conference (49th), February 9-11, 2005, Coff's Harbour, Australia 137925, Australian Agricultural and Resource Economics Society.
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