Statistical power is a measure of the replicability of a categorical hypothesis test. Formally, it is the probability of detecting an effect, if there is a true effect present in the population. Hence, optimizing statistical power as a function of some parameters of a hypothesis test is desirable. However, for most hypothesis tests, the explicit functional form of statistical power for individual model parameters is unknown; but calculating power for a given set of values of those parameters is possible using simulated experiments. These simulated experiments are usually computationally expensive. Hence, developing the entire statistical power manifold using simulations can be very time-consuming. We propose a novel genetic algorithm-based framework for learning statistical power manifolds. For a multiple linear regression $F$-test, we show that the proposed algorithm/framework learns the statistical power manifold much faster as compared to a brute-force approach as the number of queries to the power oracle is significantly reduced. We also show that the quality of learning the manifold improves as the number of iterations increases for the genetic algorithm. Such tools are useful for evaluating statistical power trade-offs when researchers have little information regarding a priori `best guesses' of primary effect sizes of interest or how sampling variability in non-primary effects impacts power for primary ones.
翻译:统计能力是绝对假设测试的可复制性的一个尺度。 形式上, 这是检测效果的概率, 如果在人口中确实存在某种效果的话。 因此, 最好将统计能力优化为假设测试某些参数的函数。 然而, 在大多数假设测试中, 单个模型参数的统计能力的明确功能形式并不为人所知; 但是, 使用模拟实验可以计算出这些参数的一组特定值的计算能力。 这些模拟实验通常在计算上花费大量费用。 因此, 利用模拟开发整个统计能力多元体可能非常耗时。 我们提议了一个基于基因算法的新框架, 用于学习统计能力多元体。 对于多线性回归, 我们显示, 提议的算法/框架比粗力方法要快得多地学习统计能力, 因为对电源或电源的查询数量显著减少。 我们还表明, 学习多元性改进的质量是基因算法的迭代数增加。 当研究人员对原始效应的先前“ 最佳猜想想力” 或非主要效果的抽样影响的信息很少时, 这些工具对于评估统计力量的权衡是有用的。