Kernal density functions to estimate parameters to simulate stochastic variables with sparse data: what is the best distribution? Conference Paper

abstract

• The purpose of this paper was to compare the goodness-of-fit for several parametric and kernal-based distributions to determine which distribution would perform well for simulating continuous random input variables whose underlying distributions were unknown. A Monte Carlo simulation procedure was developed to estimate how well some proxy distributions performed at approximating the distributions of random input variables. We conclude that without any a priori information on which to pick a probability distribution, the distribution for simulating a random input variable with limited specifications was a Parzen kernal distribution. 2010 WIT Press.

name of conference

• Risk Analysis VII

authors

• Outlaw, Joe
• Richardson, James

published proceedings

• RISK ANALYSIS VII: SIMULATION AND HAZARD MITIGATION & BROWNFIELDS V: PREVENTION, ASSESSMENT, REHABILITATION AND DEVELOPMENT OF BROWNFIELD SITES

author list (cited authors)

• Richardson, J. W., Outlaw, J. L., & Schumann, K.

citation count

• 1

complete list of authors

• Richardson, JW||Outlaw, JL||Schumann, K

publication date

• August 2010

publisher

• WITPRESS LTD.  Publisher

published in

• WIT Transactions on Information and Communication Technologies  Journal

keywords

• Kernal Distributions
• Probability Distribution Selection
• Simetar (c)
• Simulation

Digital Object Identifier (DOI)

• 10.2495/RISK100011

International Standard Book Number (ISBN) 13

• 978-1-84564-472-7

start page

• PI3

end page

• PI12

volume

• 1

URL

• http%3A%2F%2Fdx.doi.org%2F10.2495%2Frisk100011