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.

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Year of publication:	2006
Authors:	Lien, Gudbrand D. ; Hardaker, J. Brian ; Richardson, James W.
Institutions:	International Association of Agricultural Economists - IAAE
Subject:	stochastic simulation \| smoothing \| multivariate kernel estimator \| Parzen \| Research Methods/ Statistical Methods

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Extent:	application/pdf
Series:	2006 Annual Meeting, August 12-18, 2006, Queensland, Australia.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia Number 25449
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005484011