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Random variate genration is an important tool in statistical computing. Many programms for simulation or statistical computing (e.g. R) provide a collection of random variate generators for many standard distributions. However, as statistical modeling has become more sophisticated there is demand for larger classes of distributions. Adding generators for newly required distribution seems not to be the solution to this problem. Instead so called automatic (or black-box) methods have been developed in the last decade for sampling from fairly large classes of distributions with a single piece of code. For such algorithms a data about the distributions must be given; typically the density function (or probability mass function), and (maybe) the (approximate) location of the mode. In this contribution we show how such algorithms work and suggest an interface for R as an example of a statistical library. (author's abstract)Series: Preprint Series / Department of Applied Statistics and Data Processin

Topics:
MSC 65C10, R / nonuniform random variate generation / universal algorithm / automatic code generator / transformed density rejection / continuous distribution

Publisher: Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business

Year: 2003

OAI identifier:
oai:epub.wu-wien.ac.at:epub-wu-01_9f4

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