scholarly journals Virtually increased acceptance angle for efficient estimation of spatially resolved reflectance in the subdiffusive regime: a Monte Carlo study

2017 ◽  
Vol 8 (11) ◽  
pp. 4872 ◽  
Author(s):  
Matic Ivančič ◽  
Peter Naglič ◽  
Franjo Pernuš ◽  
Boštjan Likar ◽  
Miran Bürmen
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Study ◽  
Efficient Estimation ◽  
Acceptance Angle ◽  
Spatially Resolved

Estimation From Censored, Interval Data of the Median Breaking Point of Polyethylene Subjected to Stress-Cracking Tests: A Monte Carlo Study

1975 ◽  
Vol 42 (3) ◽  
pp. 607-612 ◽  
Author(s):  
L. Denby ◽  
E. B. Fowlkes ◽  
R. J. Roe
Keyword(s):  
Monte Carlo ◽  
Lognormal Distribution ◽  
Monte Carlo Study ◽  
Interval Data ◽  
Efficient Estimation ◽  
Breaking Point ◽  
Mean Square ◽  
Stress Cracking ◽  
Environmental Stress Cracking ◽  
Bias Variance

A Monte Carlo study has been carried out in order to study the properties of various alternative estimators for median breaking point of polyethylene specimens subjected to an environmental stress cracking test. Tables of bias, variance, and mean square error have been derived for different sample sizes, interval sizes, and levels of censoring data with the lognormal distribution as a model. These tables will aid in the design of experiments for efficient estimation of median breaking point.

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
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