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协方差矩阵相等性检验的蒙特卡洛比较AI 解读

The robustness of tests for equality of two covariance matrices, based on pairs of samples of size 25 from several bivariate distributions, is examined by Monte Carlo methods. The normal-theory likelihood ratio test is highly nonrobust against departures from normality. Of three asymptotically robust tests considered, the one based on a grouping of the data is superior to the others in maintaining the nominal significance level, but inferior in terms of power.

相关系数渐近稳健检验的蒙特卡洛研究AI 解读

Monte-Carlo simulation is used to compare the small-sample performance of the usual normal theory procedures for inference about correlation coefficients with that of two asymptotically robust procedures, one of which is based on a grouping of the observations and the other on the jackknife technique. The sampled distributions comprise the normal and five nonnormal distributions. The small-sample results support the conclusion based on asymptotic theory that the normal test…

方差齐性的大样本稳健检验AI 解读

Two asymptotically robust tests for equality of variances in the k-sample case are discussed: a simple X 2 test and a test based on the jack-knife procedure. Some Monte Carlo experiments suggest that these tests are reasonably robust for moderately small samples, are more powerful than Box's grouping test and perform similarly to Bartlett's test in the normal case.

相关系数渐近稳健检验的蒙特卡洛研究AI 解读

Monte-Carlo simulation is used to compare the small-sample performance of the usual normal theory procedures for inference about correlation coefficients with that of two asymptotically robust procedures, one of which is based on a grouping of the observations and the other on the jackknife technique. The sampled distributions comprise the normal and five nonnormal distributions. The small-sample results support the conclusion based on asymptotic theory that the normal test…