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This paper presents an AIS approach applied to optimize the design of an incremental discrete VSC for minimizing the generalized minimum variance strategy.
Multiple mating is a low variance strategy that works more often, but with reduced effect.
From the helping allele perspective, single mating is a high variance strategy that pays off big, but rarely.
We tested our study hypotheses a repeated measures analysis of variance strategy using Statistical Package for the Social Sciences (SPSS) version 21.
This method uses an inverse variance strategy to weight the association statistics according to their effect size estimates (i.e., beta coefficients from linear regression) and their standard errors from the discovery and validation cohorts of patients.
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The work of Li and Zhou (2006) reveals the high opportunity of a Markowitz mean-variance strategy hitting the expected return target before the maturity date.
Indeed, under design-based inference there is a lack of optimal results, in the sense that it is not possible to determine the minimum-variance strategy, as is customary in model-based approaches (e.g. Thompson 2002, Chapter 9).
In addition, agents in the simple task select high-variance strategies more frequently.
The increased use of high-variance strategies in the simple task occurs because agents with low capabilities use the strategy more frequently.
If the lack of a sleep benefit for generalization is not due to insensitivity or variance in strategy use, how can we interpret this null result?
The basis of the variance reduction strategy is to appropriately augment the governing system equations and then weakly replace the stochastic forcing function (which is typically a filtered white noise process) through a set of variance-reduced functions.
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