An Efficient Randomized Quasi-Monte Carlo Algorithm for the Pareto Distribution
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This paper studies a new randomized quasi-Monte Carlo method for estimating the mean and variance of the Pareto distribution. In many Monte Carlo simulations, there are some stability problems for estimating the population Pareto variance by using the sample variance. In this paper, we propose a randomized quasi-random number generator [quasi- RNG] to generate Pareto random samples, such that the sample mean and sample variance estimators gain more efficiency. The efficiency of this generator relative to the popular linear congruential random number generator [LC-RNG] is studied by using the simulation mean square errors. We also compare the results of the Kolmogorov-Smirnov goodness-of-fit tests using these two sample generators.
Copyright 2007, Walter de Gruyter
Articles in the same Issue
- An Efficient Randomized Quasi-Monte Carlo Algorithm for the Pareto Distribution
- Comparison of Time–to–Event Data for Clinical Trials
- Multi-step Richardson-Romberg Extrapolation: Remarks on Variance Control and Complexity
- A Fast Stratified Sampling Simulation of Coagulation Processes
- On Global Sensitivity Indices: Monte Carlo Estimates Affected by Random Errors
Articles in the same Issue
- An Efficient Randomized Quasi-Monte Carlo Algorithm for the Pareto Distribution
- Comparison of Time–to–Event Data for Clinical Trials
- Multi-step Richardson-Romberg Extrapolation: Remarks on Variance Control and Complexity
- A Fast Stratified Sampling Simulation of Coagulation Processes
- On Global Sensitivity Indices: Monte Carlo Estimates Affected by Random Errors
