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We present a solvent-based sample averaging method designed to solve spatial heterogeneity problems resulting from the presence of contaminant particles.
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Moreover, we also need to apply the sample average method to tackle the value of α − E ( ζ k i ).
Two methods for estimation of the variances and covariances were proposed: the sample average method and bootstrap estimation.
It has been shown [ 13] that provided the distances derived from sequences satisfy the maximum likelihood criterion, their distribution is approximately multivariate normal, which allows to estimate their variances and covariances using the sample average method [ 13].
Healthcare utilization and costs were characterized using Kaplan-Meier sample average methods.
The Kaplan-Meier Sampling Average method was used to estimate these costs; 95% CIs were estimated using nonparametric bootstrapping.
The Kaplan-Meier Sampling Average method was used to calculate expected lifetime healthcare costs in order to handle bias introduced by censoring [ 17].
To explore suitable solutions in a reasonable time, a solving procedure comprised of the (1) sample average approximation method, (2) Bezdek fuzzy clustering method and (3) Benders decomposition algorithm is developed.
Sample average approximation method was used to solve the model.
We employ a smoothing technique and a sample average approximation method to deal with the problems.
At each iteration, the sample average approximation method is applied to the NLP sub-problem and MILP master problem.
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