This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 653
Type: Topic Contributed
Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #308957
Title: Estimating Parameters for Rice-Distributed Time Series Observations with Applications to fMRI
Author(s): Daniel W. Adrian*+ and Ranjan Maitra and Daniel B. Rowe
Companies: Iowa State University and Iowa State University and Marquette University
Address: 1400 Gateway Hills Park Dr, Ames, IA, 50014,
Keywords: fMRI ; time series ; EM algorithm ; Rice distribution
Abstract:

Functional MRI data sets consist of a time series of measurements at each voxel of the brain. Each measurement is inherently complex-valued, with independent Gaussian real and imaginary components, making the magnitude time series, the usual object of statistical analysis, Rice-distributed. Most analyses of fMRI magnitude time series involve the general linear model with Gaussian-distributed, autocorrelated errors. We propose its counterpart, using the correct distribution for the magnitude time series, a Rice-distributed regression model with AR(p) errors. We illustrate estimation of its parameters via the EM algorithm, two methods for detecting the AR order, and calculation of approximate standard errors. We validate the resulting testing procedure for brain activation using simulated and real data.


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