Properties of the rank transformation in factorial analysis of covariance

DOI10.1080/03610910008813654zbMath1008.62654OpenAlexW2095271533MaRDI QIDQ4787636

Todd C. Headrick, Shlomo S. Sawilowsky

Publication date: 2000

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610910008813654

zbMATH Keywords

powernonparametrictype I errordesigned experiments

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Factorial statistical designs (62K15)

Related Items (9)

The power method transformation: its probability density function, distribution function, and its further use for fitting data ⋮ The t-transformed power method distributions for simulating univariate and multivariate non-normal distributions ⋮ A logistic \(L\)-moment-based analog for the Tukey \(g-h\), \(g\), \(h\), and \(h-h\) system of distributions ⋮ Rank repeated measures analysis of covariance ⋮ Comparison of aligned Friedman rank and parametric methods for testing interactions in split-plot designs ⋮ Testing factor–covariate interaction in rank repeated-measures analysis of covariance models ⋮ Simulating Controlled Variate and Rank Correlations Based on the Power Method Transformation ⋮ A Method for Simulating Correlated Non-Normal Systems of Linear Statistical Equations ⋮ Fast fifth-order polynomial transforms for generating univariate and multivariate nonnormal distributions.

Uses Software

• IMSL Numerical Libraries
• SAS

Cites Work

• Unnamed Item
• A method for simulating non-normal distributions
• Simulating correlated multivariate nonnormal distributions: extending the Fleishman power method
• Limitations of the rank transform statistic in tests for interactions
• The Rank Transform Method in Some Two-Factor Designs
• RANK TESTS IN 2X2 DESIGNS
• Rank Transformations as a Bridge Between Parametric and Nonparametric Statistics
• A note on the rank transform for interactions
• A power study of a rank transform for the two‐way classification model when interaction may be present
• A study of the power of the rank transform test in a 2³factorial experiment
• Analysis of Covariance Based on General Rank Scores

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