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双响应均有缺失时相关变量方差相等的检验AI 解读

Testing the hypothesis of equality of the variances is considered when sampling from a bivariate normal distribution with means μ1 and μ2, variances and, and correlation coefficient ρ, where some observations on either of the variables are missing. The null distributions of the proposed statistics are obtained in terms of well-known distributions. The empirical powers of the statistics are computed for different values of and ρ, and are compared with those obtained from a…

双响应均有缺失时相关变量的方差相等检验AI 解读

Testing the hypothesis of equality of the variances is considered when sampling from a bivariate normal distribution with means μ1 and μ2, variances and, and correlation coefficient ρ, where some observations on either of the variables are missing. The null distributions of the proposed statistics are obtained in terms of well-known distributions. The empirical powers of the statistics are computed for different values of and ρ, and are compared with those obtained from a…

摘要暂缺

两个响应均有缺失时相关变量的均值相等检验AI 解读

A statistic is proposed for testing the hypothesis of equality of the means of a bivariate normal distribution when observations are missing on both variates. The distribution of the statistic is approximated by a Student's t distribution under the null hypothesis. The expected squared lengths of the confidence intervals for the mean difference are used to measure the increased precision of our statistic relative to the conventional paired t test.

存在缺失观测时多元正态均值向量似然比检验的功效AI 解读

The noncentral distribution of the generalized likelihood ratio statistic for testing hypotheses on the mean vector of a multivariate normal population is derived when a subset of the variates does not have observations on some sampling units, and is used to derive the power of the test for particular values of the noncentrality parameter. The accuracy of the results is assessed by comparing them with the known power when the sample is complete.

缺失观测下多元正态均值向量似然比检验的功效AI 解读

The noncentral distribution of the generalized likelihood ratio statistic for testing hypotheses on the mean vector of a multivariate normal population is derived when a subset of the variates does not have observations on some sampling units, and is used to derive the power of the test for particular values of the noncentrality parameter. The accuracy of the results is assessed by comparing them with the known power when the sample is complete.