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用于同时比较多个指数分布位置参数的新极差统计量AI 解读

Let there be k independent exponential populations with unknown and possibly unequal location parameters, and with a common but unknown scale parameter. A complete random sample of common size n is drawn from each population. Based on those k samples, this paper proposes a new range statistic for comparing these exponential location parameters simultaneously.

比较多个指数分布位置参数的新极差统计量AI 解读

Let there be k independent exponential populations with unknown and possibly unequal location parameters, and with a common but unknown scale parameter. A complete random sample of common size n is drawn from each population. Based on those k samples, this paper proposes a new range statistic for comparing these exponential location parameters simultaneously.

重复测量设计中最大均值的最优置信区间AI 解读

A single-stage sampling procedure is proposed for obtaining an optimal confidence interval on the largest, or smallest, mean of k variates in a repeated measurements design. The variates are assumed to obey a multivariate normal distribution with common and known correlation coefficient and with the common variance being known or unknown. Percentage points to apply this procedure are also provided.

重复测量设计中最大均值的最优置信区间AI 解读

A single-stage sampling procedure is proposed for obtaining an optimal confidence interval on the largest, or smallest, mean of k variates in a repeated measurements design. The variates are assumed to obey a multivariate normal distribution with common and known correlation coefficient and with the common variance being known or unknown. Percentage points to apply this procedure are also provided.

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