Computer-Intensive Methods for Tests About the Mean of an Asymmetrical Distribution
针对偏态分布均值单侧检验，研究发现Johnson方法优于t检验，但在偏度大且样本量小时仍不准确；本文提出基于自助重抽样的计算机密集型检验程序，蒙特卡洛模拟表明其稳健性更好且能降低两类错误概率。
Abstract For one-sided tests about the mean of a skewed distribution, the t test is asymptotically robust for validity; however, it can be quite inaccurate and inefficient with small sample sizes. Results presented here confirm that a procedure due to Johnson should be preferred to the t test when the parent distribution is asymmetrical, because it reduces the probability of type I error in cases where the t test has an inflated type I error rate and it is more powerful in other situations. But if the skewness is severe and the sample size is small, then Johnson's test can also be appreciably inaccurate. For such situations, computer-intensive test procedures using bootstrap resampling are proposed, and with an extensive Monte Carlo study it is shown that these procedures are remarkably robust and can result in reduced probabilities of type I and type II errors compared to Johnson's test.