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Published in:
Computational Probability Read chapter Read first chapter

2017 | OriginalPaper | Chapter

4. Transformations of Random Variables

Authors : John H. Drew, Diane L. Evans, Andrew G. Glen, Lawrence M. Leemis

Published in: Computational Probability

Publisher: Springer International Publishing

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Abstract

This chapter presents a generalized version of the univariate change-of-variable technique for transforming continuous random variables. Extending a theorem from Casella and Berger [16] for many–to–1 transformations, we consider more general univariate transformations. Specifically, the transformation can range from 1–to–1 to many–to–1 on various subsets of the support of the random variable of interest. We also present an implementation of the theorem in APPL and present four examples.

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previous chapter Data Structures and Simple Algorithms
next chapter Bivariate Transformations of Random Variables
Literature
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Metadata
Title
Transformations of Random Variables
Authors
John H. Drew
Diane L. Evans
Andrew G. Glen
Lawrence M. Leemis
Copyright Year
2017
Book
Computational Probability

Print ISBN: 978-3-319-43321-9

Electronic ISBN: 978-3-319-43323-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-43323-3_4