
Mathematical Statistics with Mathematica
Springer (Publisher)
1st Edition
Published on 8. March 2002
Book
Mixed media product
XIII, 481 pages
978-0-387-95234-5 (ISBN)
Description
This path-breaking book presents a unified approach for doing
mathematical statistics with Mathematica. The included mathStatica
software builds upon Mathmatica's symbolic engine to create a
sophisticated toolset specially designed for doing mathematical
statistics. With mathStatica, students can easily solve difficult
statistical problems, while the professional statistician will be able
to tackle tricky multivariate distributions, generating functions,
inversion theorems, symbolic ML estimation, unbiased estimation, etc.
The mathStatica software is wonderfully easy to use, and yet so
powerful that it can find corrections to mainstream reference texts
and solve new problems in seconds. This book is the ideal companion
for researchers and students in statistics, econometrics, engineering,
physics, psychometrics, economics, finance, biometrics, and the social
sciences, across both the pure and applied domains.
The book contains two cross-platform CDs, which run on Windows, Mac,
Linux, and most flavours of UNIX:
CD 1-mathStatica CD-ROM containing:
* mathStatica: The Application Pack for mathematical statistics
* live interactive book that is identical to the printed text.
* hundreds of live examples, animations and illustrations
* custom Mathematica palettes
CD2-Mathematica v4 (trial CD): for readers who are new to
Mathematica.
mathStatica replaces dozens of reference works, extending analysis to
problems of arbitrary high order. Features include:
* a complete suite of functions for manipulating probability density
More details
Series
Language
English
Place of publication
New York, NY
United States
Target group
College/higher education
Research
Illustrations
134 s/w Abbildungen
134 black & white illustrations
Dimensions
Height: 230 mm
Weight
1100 gr
ISBN-13
978-0-387-95234-5 (9780387952345)
Schweitzer Classification
Content
Introduction * Continuous Random Variables * Discrete Random Variables
* Distributions of Functions of Random Variables * Systems of
Distributions * Multivariate Distributions * Moments of Sampling
Distributions * Asymptotic Theory * Statistical Decision Theory *
Unbiased Parameter Estimation * Principles of Maximum Likelihood
Estimation * Maximum Likelihood Estimation in Practice