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.


Issue Date:
2006
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/25449
Total Pages:
17
JEL Codes:
Q12; C8
Series Statement:
Poster Paper




 Record created 2017-04-01, last modified 2017-08-24

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