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We also consider the effect of each statistical constraint on the decision variables and on the expected cost.
They can be selected according to an economic-statistical objective: an expected total cost per unit of time incurred during production is minimized subject to a statistical constraint limiting the number of false alarms issued by the control chart.
In particular, we detail a low-complexity scheduling algorithm, which can be employed with different objective functions, exemplified here by two approaches: (1) maximizing the sum rate of the network; and (2) maximizing the number of users served, given a statistical constraint on the received rate per user.
This process places a strong statistical constraint on what ancestral states are permissible.
This statistical constraint creates a further challenge for DNA-array technology where the number of features in arrays is in thousands while tissue samples are available in limited number.
Because the organisms we have chosen are extremely similar, leading to very few differences between the two genomes, the advantage of using a stringent statistical constraint for discarding false positives will put aside false negatives under conditions where we need to explore as many paths as possible.
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Risk-based design methodology has been extended to incorporate statistical constraints also.
Statistical constraints generally prevent these from being uncovered fully during the analysis.
The orbit averaging is useful in reducing statistical noise and relaxes the statistical constraints for kinetic simulation.
Statistical constraints were simultaneously set to obtain levels of independent variables that optimized the P-EAM formulation.
We therefore propose to control the process using multivariate T2 control charts economically designed with the addition of statistical constraints, a design method called economic-statistical design.
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