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A logistic MLM was appropriate for modeling our data and testing our hypotheses because it uses robust estimation procedures appropriate for partitioning variance and error structures in mixed and nested designs (repeated measures nested within individuals in this case) for binary outcomes (choosing which oil rig would receive more damage in this case).
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It used robust and clinically relevant outcomes.
Moreover, it is probably robust because it uses independent predictors identified by multivariate analysis.
We choose Matras' first classifier because it uses a branch and bound search under several geometric constraints that we found robust.
PAM is very similar to the k-means algorithm, chosen here because it uses median data points to determine cluster centroids instead of the mean, making it more robust to outliers.
We calculated 95% CIs using robust variance estimates because using weights induces within-subject correlation (Hernán et al. 2000).
Because of the nesting of block groups within counties, SEs were computed using robust variance estimation.
The basis set 6-311G was used, because it is robust, also covers iodine and moreover, Pople basis sets are very suitable for EEM parameterization.
The DIXON fat-suppression technique is increasingly being used because it gives robust, homogeneous fat suppression and can be combined with high-resolution T2-weighted images, giving a better signal-to-noise ratio compared to the STIR sequence, without increasing the scan time [20].
The simulation model was used because it is robust in cases with small sample sizes and is more appropriate for comparing the number of correct assignments in dual- versus single-isotope markers.
This bilateral deprivation protocol was used because it generates robust plasticity in a short period of time (Clem & Barth, 2006; Glazewski et al., 2007).
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CEO of Professional Science Editing for Scientists @ prosciediting.com