Abstract
Worsley et al. (2002) propose a practical approach to multiple-subject functional MRI data analyses which uses the EM algorithm to estimate the between-subject variance component at each voxel. The main result of this article is a demonstration that the much more efficient Newton-Raphson algorithm can be reliably used for these calculations. This result follows from an extension of a simple algorithm proposed by Mandel and Paule (1970) for the one-way unbalanced ANOVA model, two variants of which have been shown to be equivalent to modified ML and REML, in which the “modification” is that the within-subject variances as treated as known.
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References
Mandel, J. and Paule, R. C. (1970). “Inter lab oratory Evaluation of a Material with Unequal Numbers of Replicates,” Analytical Chemistry, 42, 1194–1197.
Rukhin, A. L., Biggerstaff, B. J. and Vangel, M. G. (2000). “Restricted Maximum Likelihood Estimation of a Common Mean and the Mandel-Paule Algorithm,” Journal of Statistical Planning and Inference, 83, 319–330.
Tagamets M.-A. and Horwitz, B. (2000). “A Model of Working Memory: Bridging the Gap Between Electrophysiology and Human Brain Imaging,” Neural Networks, 13, 941–952.
Vangel, M. G. and Rukhin, A. L. (1999). “Maximum-Likelihood Analysis for Heteroscedastic One-Way Random Effects ANOVA in Interlaboratory Studies,” Biometrics, 55, 302–313.
Worsley, K. J., Liao, C. H., Aston, J., Petre, V., Duncan, G. EL, Morales, F., and Evans, A. C. (2002). “A General Statistical Analysis for fMRI Data,” Neuro Image, 15, 1–15.
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© 2004 Springer-Verlag Berlin Heidelberg
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Vangel, M.G. (2004). Combining Functional MRI Data on Multiple Subjects. In: Banks, D., McMorris, F.R., Arabie, P., Gaul, W. (eds) Classification, Clustering, and Data Mining Applications. Studies in Classification, Data Analysis, and Knowledge Organisation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17103-1_44
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DOI: https://doi.org/10.1007/978-3-642-17103-1_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22014-5
Online ISBN: 978-3-642-17103-1
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