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Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Measuring multivariate association and beyond.

Julie Josse¹, Susan Holmes²

¹Department of Statistics, Agrocampus Ouest - INRIA, Saclay Paris Sud University, France.

Statistics Surveys

|October 31, 2017

Summary

This summary is machine-generated.

This study surveys methods for measuring matrix association, including the RV coefficient and distance covariance (dCov). It highlights exploring patterns after confirming dependence, offering insights for analyzing complex data.

Keywords:

HHG test RV coefficient dCov coefficient distance matrix k nearest-neighbor graph measures of association between matrices multi-block data analyses permutation tests tests of independence

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Published on: September 17, 2019

Area of Science:

Multivariate statistics
Data analysis

Background:

Traditional correlation coefficients are limited to pairwise variable analysis.
Generalizations exist for measuring matrix association, such as the RV coefficient and distance covariance (dCov).
Exploring patterns of association after testing for dependence is often overlooked.

Purpose of the Study:

To survey various measures of dependence between random vectors and tests of independence.
To emphasize connections and differences between existing approaches.
To present recent improvements enhancing statistical properties and interpretability.

Main Methods:

Review of generalized correlation coefficients like RV and dCov.
Discussion of statistical tests for independence.
Exploration of multi-table approaches for heterogeneous data.

Main Results:

Provides definitions and comparisons of different association measures.
Highlights recent advancements in statistical properties and interpretation.
Demonstrates applications on real-world datasets.

Conclusions:

Offers a comprehensive overview of matrix association measures and independence tests.
Suggests strategies for summarizing heterogeneous multi-block data.
Identifies future research directions in statistical dependence analysis.