Modelling Probability Distributions from Data and its Influence on Simulation

Hörmann, Wolfgang and Bayar, Onur (2000) Modelling Probability Distributions from Data and its Influence on Simulation. Preprint Series / Department of Applied Statistics and Data Processing, 29. Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, Vienna.


Download (175kB)


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. (author's abstract)

Item Type: Paper
Additional Information: in: I. Troch and F. Breitenecker (eds.), Proceedings IMACS Symposium on Mathematical Modeling, Argesim Report No. 15, pages 429 - 435, 2000
Keywords: random number generation / kernel density estimation / smoothed bootstrap / simulation
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 10 Jul 2006 09:52
Last Modified: 22 Oct 2019 00:41


View Item View Item


Downloads per month over past year

View more statistics