Hypothesis Testing for Network Data with Power Enhancement

08/11/2019
by   Yin Xia, et al.
0

Comparing two population means of network data is of paramount importance in a wide range of scientific applications. Most existing network inference solutions focus on the scenario when the observed data are vectors or matrices, and formulate the network comparison problem as comparing two covariance or precision matrices under a normal or matrix normal distribution. Moreover, many suffer from a limited power under a small sample size. In this article, we tackle the problem of network comparison when the data come in a different format, i.e., in the form of a collection of symmetric matrices, each of which encodes the network structure of an individual subject. Such data format commonly arises in applications such as brain connectivity analysis and clinical genomics. We no longer require the underlying data to follow a normal distribution, but instead impose some moment conditions that are easily satisfied for numerous types of network data. Furthermore, we propose a power enhancement procedure, and show that it can control the false discovery, while it has the potential to substantially enhance the power of the test. We investigate the efficacy of our testing procedure through both an asymptotic analysis, and a simulation study under a finite sample size. We further illustrate our method with an example of brain structural connectivity analysis.

READ FULL TEXT

page 24

page 26

research
05/10/2020

Testing Mediation Effects Using Logic of Boolean Matrices

Mediation analysis is becoming an increasingly important tool in scienti...
research
08/11/2020

Test for mean matrix in GMANOVA model under heteroscedasticity and non-normality for high-dimensional data

This paper is concerned with the testing bilateral linear hypothesis on ...
research
05/31/2020

Fisher's combined probability test for high-dimensional covariance matrices

Testing large covariance matrices is of fundamental importance in statis...
research
12/07/2017

Set-based differential covariance testing for high-throughput data

The problem of detecting changes in covariance for a single pair of feat...
research
10/23/2017

A Test for Separability in Covariance Operators of Random Surfaces

The assumption of separability is a simplifying and very popular assumpt...
research
07/20/2021

Directional testing for high-dimensional multivariate normal distributions

Thanks to its favorable properties, the multivariate normal distribution...
research
06/02/2021

Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning

In this article, we propose a new hypothesis testing method for directed...

Please sign up or login with your details

Forgot password? Click here to reset