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Optimum design of experiments for statistical inference

机译:统计推断的实验优化设计

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Purpose: To define modified optimality criteria which correctly reflect the utility of designs with respect to some common types of inference and do not have the demerits of D- and A-optimality criteria. Summary: In design of experiments, variance-based optimality criteria are commonly used, since they are readily available in software and have useful interpretations in relation to the statistical analysis of the design outcome. Multiple objectives can be met through the use of compound criteria which are now becoming popular. Fractional factorial and response surface designs are widely used for experiments in food industry, but the relatively large run-to-run variation that is caused by the use of biological materials.
机译:目的:定义修改后的最优性标准,以相对于一些常见的推断类型正确反映设计的效用,并且不具有D-和A-最优性标准的缺点。简介:在实验设计中,通常使用基于方差的最优标准,因为它们很容易在软件中获得,并且对设计结果的统计分析具有有用的解释。通过使用复合标准,可以实现多个目标,复合标准现已变得越来越流行。分数阶乘和响应曲面设计被广泛用于食品工业中的实验,但是由于使用生物材料而导致的较大的运行间差异。

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