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

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 2010

published proceedings

  • Risk Analysis VII

author list (cited authors)

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

publication date

  • January 1, 2010 11:11 AM