Clustering Methods for Delineating Regions of Spatial Stationarity

This paper seeks to further investigate data extracted by the use of Functional Magnetic Resonance Imaging (FMRI) as it is applied to brain tissue and how it measures blood flow to certain areas of the brain following the application of a stimulus. As a precursor to detailed spatial analysis of this kind of data, this paper develops methods of grouping data based on the necessary conditions for spatial statistical analysis. The purpose of this paper is to examine and develop methods that can be used to delineate regions of stationarity. One of the major assumptions used in spatial estimation is that the data field is homogeneous with respect to the mean and the covariance function. As such, any spatial estimation presupposes that these criteria are met. With respect to analyses that may be considered new or experimental, however, there is no evidence that these assumptions will hold.

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Collings, Jared M.

• clustering
• FRMI
• spatial stationarity
• SFMRI
• Statistics and Probability

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/1228

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-2227