The Sensitivity of Chi-Squared Goodness-of-Fit Tests to the Partitioning of Data
研究了皮尔逊总体拟合优度检验和安德森提出的卡方分量检验在检测位置、尺度、偏度和峰度变化时的统计功效如何随分组数量和位置变化，发现使用较少分组和不等概率分组可提高功效。
Abstract The power of Pearson's overall goodness-of-fit test and the components-of-chi-squared or “Pearson analog” tests of Anderson [Anderson, G. (1994). Simple tests of distributional form. J. Econometrics 62:265–276] to detect rejections due to shifts in location, scale, skewness and kurtosis is studied, as the number and position of the partition points is varied. Simulations are conducted for small and moderate sample sizes. It is found that smaller numbers of classes than are used in practice may be appropriate, and that the choice of non-equiprobable classes can result in substantial gains in power.