Parametric and nonparametric bootstrap methods for general MANOVA

JOURNAL OF MULTIVARIATE ANALYSIS

卷:140

Article

Konietschke, Frank¹  Bathke, Arne C.^2,3  Harrar, Solomon W.³  Pauly, Markus⁴ 

[1] Univ Texas Dallas, Dept Math Sci, Richardson, TX 75080 USA

[2] Salzburg Univ, Fachbereich Math, A-5020 Salzburg, Austria

[3] Univ Kentucky, Dept Stat, Lexington, KY 40536 USA

[4] Univ Ulm, Inst Stat, D-89081 Ulm, Germany

关键词: ANOVA;    Multivariate Behrens-Fisher problem;    Multivariate data;    Nonparametric bootstrap;    Parametric bootstrap;    Wald test;   

DOI  :  10.1016/j.jmva.2015.05.001

来源: Elsevier

【 摘 要 】

We develop parametric and nonparametric bootstrap methods for multi-factor multivariate data, without assuming normality, and allowing for covariance matrices that are heterogeneous between groups. The newly proposed, general procedure includes several situations as special cases, such as the multivariate Behrens Fisher problem, the multivariate one-way layout, as well as crossed and hierarchically nested two-way layouts. We derive the asymptotic distribution of the bootstrap tests for general factorial designs and evaluate their performance in an extensive comparative simulation study. For moderate sample sizes, the bootstrap approach provides an improvement to existing methods in particular for situations with nonnormal data and heterogeneous covariance matrices in unbalanced designs. For balanced designs, less computationally intensive alternatives based on approximate sampling distributions of multivariate tests can be recommended. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free