Generating random samples from user-defined distributions
TL;DR: This article introduces a command that generates a random sample from any user-specified distribution function using numeric methods that make this command very generic.
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Abstract: Generating random samples in Stata is very straightforward if the distribution drawn from is uniform or normal. With any other distribution, an inverse method can be used; but even in this case, the user is limited to the built- in functions. For any other distribution functions, their inverse must be derived analytically or numerical methods must be used if analytical derivation of the inverse function is tedious or impossible. In this article, I introduce a command that generates a random sample from any user-specified distribution function using numeric methods that make this command very generic.
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References
A Flexible Method For Estimating Inverse Distribution Functions In Simulation Experiments
Athanassios N. Avramidis,James R. Wilson +1 more
- 01 Oct 1989
TL;DR: In this article, a polynomial filter for the inverse of the reference distribution is estimated by constrained nonlinear regression so that the resulting inverse distribution has minimum "distance" from the empirical inverse distribution.
22
A method for computer generation of variates from arbitrary continuous distributions
Gary Ulrich,Layne T. Warson +1 more
TL;DR: In this paper, the inverse cdf transform of a continuous random variable with cumulative distribution function (cdf) is used to generate a variate from this distribution, where U is a random uniform variate.
16
•Book
Basic Statistics for Business and Economics
Earl K. Bowen,Martin Kenneth Starr +1 more
- 01 Jan 1982
TL;DR: In this article, the authors summarize the central tendency and variability probability introduction to statistical decision analysis discrete probability distributions the normal distribution and other continuous probability distributions sampling methods and sampling distributions statistical inference estimation and hypotheses testing simple linear regression and correlation multiple and curvilinear regression chi-square tests for independence and goodness-of-fit analysis of variance nonparametric tests index numbers.
•Book
Random number generation and Monte Carlo methods
James E. Gentle
- 01 Jan 1998
TL;DR: Simulating Random Numbers from a Uniform Distribution * Quality of Random Number Generation * Quasirandom Numbers * Transformations of Uniform Deviates: General Methods * Simulating Random numbers from Specific Distributions
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