abstract: Functional magnetic resonance imaging (fMRI) is used to study brain activity due
to stimuli presented to subjects in a scanner. It is important to conduct statistical
inference on such time series fMRI data obtained. It is also important to select optimal designs for practical experiments. Design selection under autoregressive models
have not been thoroughly discussed before. This paper derives general information
matrices for orthogonal designs under autoregressive model with an arbitrary number
of correlation coefficients. We further provide the minimum trace of orthogonal circulant designs under AR(1) model, which is used as a criterion to compare practical
designs such as M-sequence designs and circulant (almost) orthogonal array designs.
We also explore optimal designs under AR(2) model. In practice, types of stimuli can
be more than one, but in this paper we only consider the simplest situation with only
one type of stimuli. / Dissertation/Thesis / Masters Thesis Statistics 2017
| Identifer | oai:union.ndltd.org:asu.edu/item:44297 |
| Date | January 2017 |
| Contributors | Chen, Chuntao (Author), Stufken, John (Advisor), Reiser, Mark (Committee member), Kamarianakis, Ioannis (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 23 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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