The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study

2004 ◽  
Vol 51 (4) ◽  
pp. 807-815 ◽  
Author(s):  
Derek K. Jones
Keyword(s):  
Monte Carlo ◽  
Diffusion Tensor ◽  
Monte Carlo Study ◽  
Diffusion Tensor Mri ◽  
Sampling Schemes ◽  
Gradient Sampling

scholarly journals A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

2006 ◽  
Author(s):  
F. Prados ◽  
A. Bardera ◽  
M. Sbert ◽  
I. Boada ◽  
M. Feixas
Keyword(s):  
Monte Carlo ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Tracking Algorithm ◽  
Diffusion Tensor Mri

Anisotropic noise propagation in diffusion tensor MRI sampling schemes

2003 ◽  
Vol 49 (6) ◽  
pp. 1143-1151 ◽  
Author(s):  
P.G. Batchelor ◽  
D. Atkinson ◽  
D.L.G. Hill ◽  
F. Calamante ◽  
A. Connelly
Keyword(s):  
Diffusion Tensor ◽  
Diffusion Tensor Mri ◽  
Noise Propagation ◽  
Sampling Schemes

Analyzing Observed Composite Differences Across Groups

Methodology ◽  
2013 ◽  
Vol 9 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Holger Steinmetz
Keyword(s):  
Monte Carlo ◽  
Structural Equation Modeling ◽  
Measurement Invariance ◽  
Structural Equation ◽  
Monte Carlo Study ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Latent Mean Differences ◽  
Partial Invariance ◽  
Mean Differences

Although the use of structural equation modeling has increased during the last decades, the typical procedure to investigate mean differences across groups is still to create an observed composite score from several indicators and to compare the composite’s mean across the groups. Whereas the structural equation modeling literature has emphasized that a comparison of latent means presupposes equal factor loadings and indicator intercepts for most of the indicators (i.e., partial invariance), it is still unknown if partial invariance is sufficient when relying on observed composites. This Monte-Carlo study investigated whether one or two unequal factor loadings and indicator intercepts in a composite can lead to wrong conclusions regarding latent mean differences. Results show that unequal indicator intercepts substantially affect the composite mean difference and the probability of a significant composite difference. In contrast, unequal factor loadings demonstrate only small effects. It is concluded that analyses of composite differences are only warranted in conditions of full measurement invariance, and the author recommends the analyses of latent mean differences with structural equation modeling instead.

Detecting Between-Groups Heteroscedasticity in MMR: A Monte Carlo Study

2011 ◽  
Author(s):  
Patrick J. Rosopa ◽  
Amber N. Schroeder ◽  
Jessica Doll
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study

Monte Carlo study of 50 nm-long single and dual-gate MODFETs: suppression of short-channel effects

1993 ◽  
Vol 3 (9) ◽  
pp. 1719-1728
Author(s):  
P. Dollfus ◽  
P. Hesto ◽  
S. Galdin ◽  
C. Brisset
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Short Channel Effects ◽  
Short Channel ◽  
Channel Effects ◽  
Dual Gate

MONTE CARLO STUDY OF EPITAXIAL OVERLAYER WITH SUBSTANTIAL LATTICE MISMATCH

1987 ◽  
Vol 48 (C5) ◽  
pp. C5-199-C5-202
Author(s):  
T. MIYASAKI ◽  
K. AIZAWA ◽  
H. AOKI ◽  
C. ITOH ◽  
M. OKAZAKI
Keyword(s):  
Monte Carlo ◽  
Lattice Mismatch ◽  
Monte Carlo Study
Sign in / Sign up

Export Citation Format

Share Document