2000-01.wh-bay
Modelling Probability Distributions from Data and its Influence on Simulation
Abstract
Generating random variates as generalisation of a given sample is an important task for stochastic
simulations. The three main methods suggested in the literature are: fitting a standard distribution, constructing
an empirical distribution that approximates the cumulative distribution function and generating variates from the
kernel density estimate of the data. The last method is practically unknown in the simulation literature although
it is as simple as the other two methods. The comparison of the theoretical performance of the methods and the
results of three small simulation studies show that a variance corrected version of kernel density estimation
performs best and should be used for generating variates directly from a sample.
General Terms:
Algorithms
Key Words:
random number generation, kernel density estimation, smoothed bootstrap,
simulation
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Wolfgang.Hoermann@statistik.wu-wien.ac.at