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Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain

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Robust Analysis of Covariance

Biometrics, 1982
The simple analysis of covariance situation with two groups and one concomitant variable is considered. The parameters of this model with outliers present are estimated by the methods of at least squares and M-estimation. By use of simulation, several forms of M-estimators are compared with the least squares method.
Birch, Jeffrey B., Myers, Raymond H.
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Adjusting for demographic covariates by the analysis of covariance

Journal of Clinical and Experimental Neuropsychology, 1992
(1992). Adjusting for demographic covariates by the analysis of covariance. Journal of Clinical and Experimental Neuropsychology: Vol. 14, No. 6, pp. 981-982.
N C, Berman, S W, Greenhouse
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Analysis of Covariance Structures

Psychometrika, 1966
A general method is presented for estimating variance components when the experimental design has one random way of classification and a possibly unbalanced fixed classification. The procedure operates on a sample covariance matrix in which the fixed classes play the role of variables and the random classes correspond to observations.
R D, Bock, R E, Bargmann
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The Analysis of Covariance

Biometrics, 1948
THE WHOLE OF this discussion is based on the data of a single experiment, the details of which have been published under the title "The Effect of Atropine and Quinidine Sulphate on Atrophy and Fibrillation in Deniervated Skeletal Muscle." [1] For the present, we may adopt the view that the experiment was conducted to compare the effects of four ...
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Analysis of Covariance Algorithms

Biometrics, 1982
Computational algorithms specific to the analysis of covariance are discussed for the treatment of both balanced and unbalanced data. The use of covariance algorithms in the solution of missing data problems is also considered.
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Randomization in the Analysis of Covariance

Biometrika, 1986
When examining any linear contrast of adjusted treatment means in the analysis of covariance it is shown that balancing on the covariates is desirable in that it minimizes the model variance of the treatment contrast averaged over the randomization distribution.
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