A Generalized Univariate Change-of-Variable Transformation Technique | Zendy

Andrew G. Glen | Zendy; Lawrence M. Leemis | Zendy; John H. Drew | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

A Generalized Univariate Change-of-Variable Transformation Technique

Author(s) -

Andrew G. Glen,

Lawrence M. Leemis,

John H. Drew

Publication year - 1997

Publication title -

informs journal on computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.403

H-Index - 80

eISSN - 1526-5528

pISSN - 1091-9856

DOI - 10.1287/ijoc.9.3.288

Subject(s) - univariate , transformation (genetics) , mathematics , variable (mathematics) , random variable , inference , range (aeronautics) , statistical inference , algebra over a field , computer science , statistics , pure mathematics , artificial intelligence , multivariate statistics , mathematical analysis , biochemistry , chemistry , materials science , composite material , gene

We present a generalized version of the univariate change-of-variable technique for transforming continuous random variables. Extending a theorem from Casella and Berger [1990. Statistical Inference, Wadsworth and Brooks/Cole, Inc., Pacific Grove, CA] 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 a computer algebra system that automates the technique. Some examples demonstrate the theorem's application.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research