Practical methods for computing power in testing the multivariate general linear hypothesis

DOI10.1016/0167-9473(84)90002-1zbMath0571.65119OpenAlexW2067813945MaRDI QIDQ1061479

Bercedis L. Peterson, Keith E. Muller

Publication date: 1984

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-9473(84)90002-1

zbMATH Keywords

MANOVAnoncentral distributionsF approximationsHotelling-Lawley tracePillai-Bartlett traceWilks' lambda

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Hypothesis testing in multivariate analysis (62H15) Probabilistic methods, stochastic differential equations (65C99)

Related Items (12)

An empirical study of the type I error rate and power for some selected normal-theory and nonparametric tests of the independence of two sets of variables ⋮ Hypothesis Testing in Multivariate Linear Models with Randomly Missing Data ⋮ The distribution of the ratio of independent central wishart determinants ⋮ Calculating power for the general linear multivariate model with one or more Gaussian covariates ⋮ Exact distributions of two non-central generalized Wilks's statistics ⋮ Methods for analyzing multivariate phenotypes in genetic association studies ⋮ On Sample Size of the Kruskal-Wallis Test with Application to a Mouse Peritoneal Cavity Study ⋮ Power and sample size calculations for multivariate linear models with random explanatory variables ⋮ Regression based thresholds in principal loading analysis ⋮ Simple approximation of sample size for the bivariate normal ⋮ Power and Sample Size for Fixed-Effects Inference in Reversible Linear Mixed Models ⋮ SAMPLE SIZE FOR COMPARING LINEAR GROWTH CURVES

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