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Prerequisites
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notes
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Acknowledgements
Contents
Distribution Theory
Subsections
Prerequisites
Probability concepts assumed known
Assumed knowledge of matrices and vector spaces
Preliminaries
Introduction
Indicator Functions
Distribution Functions (cdf's)
Bivariate and Conditional Distributions
Conditional Mean and Variance
Stochastic Independence
Moment Generating Functions (mgf)
Multinomial Distribution
Transformations
Introduction
Bivariate Transformations
Multivariate Transformations (One-to-One)
Multivariate Transformations Not One-to-One
Convolutions
General Linear Transformation
Multivariate Normal Distribution
Bivariate Normal
Multivariate Normal (MVN) Distribution
Moment Generating Function
Independence of Quadratic Forms
Distribution of Quadratic Forms
The role of c.g.f.
Cochran's Theorem
Order Statistics
Introduction
Distribution of Order Statistics
Marginal Density Functions
Joint Distribution of
and
The Transformation
Examples
Non-central Distributions
Introduction
Distribution Theory of the Non-Central Chi-Square
Non-Central t and F-distributions
POWER: an example of use of non-central F
S-Plus commands
Bob Murison 2000-10-31
and 
