Simulating Multivariate Distributions with Sparse Data: A Kernal Density Smoothing Procedure

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dc.creatorLien, Gudbrand D.
dc.creatorHardaker, J. Brian
dc.creatorRichardson, James W.
dc.date2017-04-01T14:23:49Z
dc.date.accessioned2026-07-09T03:50:59Z
dc.descriptionOften 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.
dc.identifierdoi:10.22004/ag.econ.25449
dc.identifierhttps://ageconsearch.umn.edu/record/25449/files/pp060805.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/25449
dc.identifier.urihttp://hdl.handle.net/123456789/540863
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/25449
dc.titleSimulating Multivariate Distributions with Sparse Data: A Kernal Density Smoothing Procedure
dc.typeText

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