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While the vertical sampling procedure was performed using a progressive threshold, the horizontal sampling followed a bootstrapping procedure with random extraction without replacement.
The statistical analysis was performed with SAS using the glimmix procedure (with random intercept).
We provide descriptive characteristics of the sample and use multivariable analysis for each procedure with random effects logit regression.
As a way of determining the likelihood of obtaining models that explain such a large fraction of variance by chance, we compare the machine learning L1-regularized regression procedure with random permutations of six and seven genes to determine what fraction of randomly generated data sets would explain as large or larger a fraction of birth weight variance as the L1 procedure.
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Since there could be multiple passengers in the same vehicle, the SAS GLIMMIX procedure (SAS Institute Inc 2014) with random effects was used to generate odds ratios with 95%% confidence intervals using multilevel models that controlled for violation of the assumption of independence associated with multiple rear-seated occupants in the same vehicle.
The approach in [10] favorably merges the RS procedure with a random access protocol, thus obtaining a significant reduction in both the consumed energy and the occupied bandwidth.
The parallel algorithm is embedded with a multi-start heuristic which consists of a variable neighborhood descent procedure, with a random neighborhood ordering (RVND), integrated in an iterated local search (ILS) framework.
This approach is intended to keep control of the uncertainty effects on the decision criteria and reduce the computational effort in simulating RAMS+C using a MC procedure with simple random sampling.
Average over 1000 repeats of the procedure with different random numbers.
Within-study model validity was assessed using a jackknife procedure with 10 random samples of 90% of the cohort, as has been done previously (3).
ME distance trees were constructed with DNA distances set to maximum likelihood and a heuristic search procedure with 10 random input orders and TBR were employed to find the best tree.
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