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We further design an algorithm that can efficiently determine the hinterland boundaries using the sample approximation of shippers' choice probabilities.
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We use Conditional Value at Risk (CVaR) to approximate the chance constraint, and we get the approximation problem of the chance constrained stochastic programming by using the sample average approximation (SAA) method.
The variance covariance matrix Σ of univariate test statistics using the sample covariance matrix of the test statistics of all SNPs from univariate GWAS analyses as an approximation.
We use the Sample Average Approximation (SAA) approach together with Benders decomposition to provide a solution method.
The p-value was calculated using the large sample approximation.
Assuming a participation rate of around 50%, the two-sided 95% confidence interval for the participation rate will extend 1% from the observed proportion, using the large sample approximation to the binomial distribution.
Since the number of samples (window size T) is sufficiently large we use the large sample approximation for the critical level A 0.05 2 = 2.492 [45].
Spearman correlation was applied to calculate r correlation coefficient, with p-value testing the hypothesis of no correlation against the alternative that there is a nonzero correlation using the large-sample approximation.
Another option is to use the large sample approximation for power described by Liu and Liang.
Predefined sample size for Germany, Greece, Norway and Spain was 500 patients (allowing to obtain two-sided 95% confidence intervals for single proportions using the large sample normal approximation that will extend 4.4% from the observed proportion for an expected proportion of 50% [the worst possible case]).
The 95% confidence intervals of the proportions of time spent on the different tasks were obtained using the large sample normal approximation.
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