MULTIVARIATE VARIANCE COMPONENT ANALYSIS: AN APPLICATION IN TEST DEVELOPMENT

Multivariate analysis of variance test for gene set analysis

MOTIVATION Gene class testing (GCT) or gene set analysis (GSA) is a statistical approach to determine whether some functionally predefined sets of genes express differently under different experimental conditions. Shortcomings of the Fisher's exact test for the overrepresentation analysis are illustrated by an example. Most alternative GSA methods are developed for data collected from two exper...

Simultaneous Test Procedures in Multivariate Analysis of Variance

Multivariate geostatistical analysis: an application to ore body evaluation

It is now common in the mining industry to deal with several correlated attributes, which need to be jointly simulated in order to reproduce their correlations and assess the multivariate grade risk reasonably. Approaches to multivariate simulation which remove the correlation between attributes of interest prior to simulate and then re-impose the relationship afterward have been gaining popula...

Multivariate Analysis of Variance

We provide an expository presentation of multivariate analysis of variance (MANOVA) for both consumers of research and investigators by capitalizing on its relation to univariate analysis of variance models. We address several questions: (a) Why should one use MANOVA. 9 (b) What is the structure of MANOVA? (C) How are MANOVA test statistics obtained and interpreted? (d) How are MANOVA follow-up...

Multivariate Analysis of Variance

1. Introduction In many agricultural experiments, generally the data on more than one character is observed. One common example is grain yield and straw yield. The other characters on which the data is generally observed are the plant height, number of green leaves, germination count, etc. The analysis is normally done only on the grain yield and the best treatment is identified on the basis of...