Hypothesis Testing for Two Sample Comparison of Network Data

06/26/2021
by   Han Feng, et al.
0

Network data is a major object data type that has been widely collected or derived from common sources such as brain imaging. Such data contains numeric, topological, and geometrical information, and may be necessarily considered in certain non-Euclidean space for appropriate statistical analysis. The development of statistical methodologies for network data is challenging and currently at its infancy; for instance, the non-Euclidean counterpart of basic two-sample tests for network data is scarce in literature. In this study, a novel framework is presented for two independent sample comparison of networks. Specifically, an approximation distance metric to quotient Euclidean distance is proposed, and then combined with network spectral distance to quantify the local and global dissimilarity of networks simultaneously. A permutational non-Euclidean analysis of variance is adapted to the proposed distance metric for the comparison of two independent groups of networks. Comprehensive simulation studies and real applications are conducted to demonstrate the superior performance of our method over other alternatives. The asymptotic properties of the proposed test are investigated and its high-dimensional extension is discussed as well.

READ FULL TEXT
research
05/27/2022

New graph-based multi-sample tests for high-dimensional and non-Euclidean data

Testing the equality in distributions of multiple samples is a common ta...
research
04/29/2019

Individualized Treatment Selection: An Optimal Hypothesis Testing Approach In High-dimensional Models

The ability to predict individualized treatment effects (ITEs) based on ...
research
09/09/2022

Quantiles, Ranks and Signs in Metric Spaces

Non-Euclidean data is currently prevalent in many fields, necessitating ...
research
05/29/2022

2-Dimensional Euclidean Preferences

A preference profile with m alternatives and n voters is 2-dimensional E...
research
02/17/2021

Reviews: Topological Distances and Losses for Brain Networks

Almost all statistical and machine learning methods in analyzing brain n...
research
06/24/2021

Two-sample tests for repeated measurements of histogram objects with applications to wearable device data

Repeated observations have become increasingly common in biomedical rese...
research
06/28/2023

Fast Marching Energy CNN

Leveraging geodesic distances and the geometrical information they conve...

Please sign up or login with your details

Forgot password? Click here to reset