Collaborative Research: Using Prior Kurtosis Information to Improve Confidence Intervals for Standard Deviations

本研究将应用整合分析与Theil-Goldberger混合估计法来改善样本变异数的标准误。 该研究将展示如何从以前的研究峰度信息可以结合使用一个新的荟萃分析峰度估计，这个估计是比标准的荟萃分析峰度估计偏差较小。 该研究还将展示如何应用Theil-Goldberger方法将元分析峰度估计与样本峰度估计混合，以获得样本方差的渐近分布自由标准误。 然后将渐近分布自由标准误差用于标准差的三个基本置信区间：1）单个标准差的置信区间，2）独立样本设计中两个标准差比率的置信区间，以及3）配对样本设计中两个标准差比率的置信区间。 本研究将检验三种基本置信区间在几种样本大小和广泛的实际分布下的小样本覆盖概率，标准差或标准差比的置信区间可以用来回答心理测量学、行为遗传学和质量控制中的基本问题。 目前可用的方法是基于不切实际的假设，如正态性或等峰度，并且如果这些假设以使用标准诊断工具难以检测的微妙方式被违反，则可能表现不佳。 这项研究的结果将为科学家提供一套新的统计工具，可用于评估广泛应用中的变异性，并有望在现实条件下表现良好。

This research will apply meta-analysis and the Theil-Goldberger mixed estimation method to improve the standard error of a sample variance. The study will show how kurtosis information from previous studies can be combined using a new meta-analytic kurtosis estimator and that this estimator is less biased than the standard meta-analytic kurtosis estimator. The study also will show how to apply the Theil-Goldberger method to mix a meta-analytic kurtosis estimate with a sample kurtosis estimate to obtain an asymptotic distribution-free standard error of a sample variance. The asymptotic distribution-free standard error will then be used in three basic confidence intervals for standard deviations: 1) a confidence interval for a single standard deviation, 2) a confidence interval for a ratio of two standard deviations in independent-samples designs, and 3) a confidence interval for a ratio of two standard deviations in paired-samples designs. This research will examine the small-sample coverage probabilities of the three basic confidence intervals for several sample sizes and a wide range of realistic distributions.Confidence intervals for a standard deviation or a ratio of standard deviations can be used to answer fundamental questions in psychometrics, behavior genetics, and quality control. Currently available methods are based on unrealistic assumptions, such as normality or equal kurtosis, and can perform poorly if these assumptions are violated in subtle ways that would be difficult to detect using standard diagnostic tools. The results of this study will provide scientists with a new set of statistical tools that can be used to assess variability in a wide range of applications and can be expected to perform well under realistic conditions.