Missing data estimators in the general linear model: an evaluation of simulated data as an experimental design

1985 ◽  
Vol 14 (2) ◽  
pp. 371-394 ◽  
Author(s):  
Alexander Basilevsky ◽  
Donald Sabourin ◽  
Derek Hum ◽  
Andy Anderson
Keyword(s):  
Missing Data ◽  
Experimental Design ◽  
Linear Model ◽  
Simulated Data ◽  
General Linear Model ◽  
General Linear

scholarly journals An Association Rule General Analytic System (ARGAS) for hypothesis testing and data mining applications

2018 ◽  
Author(s):  
Rick Parente
Keyword(s):  
Data Mining ◽  
Experimental Design ◽  
Hypothesis Testing ◽  
Linear Model ◽  
Association Rule ◽  
General Linear Model ◽  
Experimental Designs ◽  
General Linear ◽  
Testing Hypotheses ◽  
Alternative Method

<div>The primary purpose of this study was to evaluate the use of an Association Rule General Analytic System (ARGAS) versus the General Linear Model (GLM) for hypothesis testing. Results indicate that the ARGAS provides an better alternative method for testing hypotheses when the assumptions of the GLM are violated. The ARGAS approach can be used with any experimental design to which the GLM can be applied. ARGAS is free of the usual assumptions of the GLM. A second purpose of the study was to illustrate how the ARGAS can be used for hypothesis testing with commonly used experimental designs.</div><div><br></div><div> </div>

scholarly journals An Association Rule General Analytic System (ARGAS) for hypothesis testing and data mining applications

2018 ◽  
Author(s):  
Rick Parente
Keyword(s):  
Data Mining ◽  
Experimental Design ◽  
Hypothesis Testing ◽  
Linear Model ◽  
Association Rule ◽  
General Linear Model ◽  
Experimental Designs ◽  
General Linear ◽  
Testing Hypotheses ◽  
Alternative Method

<div>The primary purpose of this study was to evaluate the use of an Association Rule General Analytic System (ARGAS) versus the General Linear Model (GLM) for hypothesis testing. Results indicate that the ARGAS provides an better alternative method for testing hypotheses when the assumptions of the GLM are violated. The ARGAS approach can be used with any experimental design to which the GLM can be applied. ARGAS is free of the usual assumptions of the GLM. A second purpose of the study was to illustrate how the ARGAS can be used for hypothesis testing with commonly used experimental designs.</div><div><br></div><div> </div>

EEG-fMRI: Including sleep-induced potentials in the general linear model improves spatial accordance with epileptic focus

2010 ◽  
Vol 41 (02) ◽  
Author(s):  
J Möhring ◽  
D Coropceanu ◽  
F Möller ◽  
S Wolff ◽  
R Boor ◽  
...  
Keyword(s):  
Linear Model ◽  
General Linear Model ◽  
General Linear ◽  
Epileptic Focus
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