Contingency Table Approach to Nonparametric Testing
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A01=D.J. Best
A01=J.C.W. Rayner
advanced contingency table analysis
Anderson Darling Statistic
Anderson's Statistic
Anderson’s Statistic
Author_D.J. Best
Author_J.C.W. Rayner
Balanced Incomplete Block Design
Bivariate Moment
carlo
Category=PBT
chi-squared analysis
Contingency Table
CRC Press LLC
Data Set
Durbin Tests
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Friedman's Test
Friedman’s Test
goodness of fit testing
hypothesis
Kruskal Wallis Statistic
Kruskal Wallis Test
Log Linear Models
log-linear
model
monte
Monte Carlo methods
nonparametric statistics
nuisance
Nuisance Parameters
Omnibus Tests
parameter
Parametric Log Linear Models
Pearson's X2 Statistic
Pearson’s X2 Statistic
Probability Density Functions
Product Multinomial Model
Quadratic Effects
randomized block design
Smooth Model
Smooth Tests
statistic
statistical inference techniques
Target Distribution
Testing H0
wald
Wald Tests
Wald Type Statistic
Product details
- ISBN 9781584881612
- Weight: 526g
- Dimensions: 156 x 234mm
- Publication Date: 07 Dec 2000
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Most texts on nonparametric techniques concentrate on location and linear-linear (correlation) tests, with less emphasis on dispersion effects and linear-quadratic tests. Tests for higher moment effects are virtually ignored. Using a fresh approach, A Contingency Table Approach to Nonparametric Testing unifies and extends the popular, standard tests by linking them to tests based on models for data that can be presented in contingency tables.
This approach unifies popular nonparametric statistical inference and makes the traditional, most commonly performed nonparametric analyses much more complete and informative. It also makes tied data easily handled, and almost exact Monte Carlo p-values can be obtained. With data in contingency tables, one can then calculate a Pearson-type, chi-squared statistic and its components. For univariate data, the initial tests based on these components detect mean differences between treatments. For bivariate data, they detect correlations. This approach leads to tests that detect variance, skewness, and higher moment differences between treatments with univariate data, and higher bivariate moment differences with bivariate data.
Although the methods advanced in this book have their genesis in traditional nonparametrics, incorporating the power of modern computers makes the approach more complete and more valid than previously possible. The authors' unified treatment and readable style make the subject easy to follow and the techniques easily implemented, whether you are a fledgling or a seasoned researcher.
