Modelling Probability Distributions from Data and its Influence on Simulation

Wolfgang Hörmann and Onur Bayar


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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