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剂量反应与毒性研究中无需多重性调整的逐步置信区间AI 解读

Not all simultaneous inferences need multiplicity adjustment. If the sequence of individual inferences is predefined, and failure to achieve the desired inference at any step renders subsequent inferences unnecessary, then multiplicity adjustment is not needed. This can be justified using the closed testing principle to test appropriate hypotheses that are nested in sequence, starting with the most restrictive one. But what hypotheses are appropriate may not be obvious in…

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逐步决策程序与置信集的关系AI 解读

This article considers the relationship between two standard methods of inference on a set of A: unknown parameters, namely stepwise decision procedures and the construction of simultaneous confidence intervals. Illustrative examples with k = 2 are discussed for one-sided step-down and step-up decision procedures and for a two-sided step-down decision procedure. For these examples with normal distributional assumptions, a set of confidence intervals associated with the…

逐步决策程序与置信集之间的关系AI 解读

This article considers the relationship between two standard methods of inference on a set of A: unknown parameters, namely stepwise decision procedures and the construction of simultaneous confidence intervals. Illustrative examples with k = 2 are discussed for one-sided step-down and step-up decision procedures and for a two-sided step-down decision procedure. For these examples with normal distributional assumptions, a set of confidence intervals associated with the…

与生物等效性检验相关的置信区间AI 解读

Previously, the connection between bioequivalence tests and their associated confidence intervals was not well understood. In fact, the α-level two one-sided tests approach was thought to be associated with a 1−2α confidence interval. In this paper, we build up connections which allow us to construct 1−α confidence intervals from α-level tests and vice versa. When applied to the α-level two one-sided tests, the resultant 1−-α confidence intervals are properly contained in…

与最佳者多重比较的约束同时置信区间AI 解读

For comparing $k$ treatment effects $\theta_1, \theta_2, \cdots \theta_k$, often the parameters of primary interest are $\theta_i - \max_{j\neq i}\theta_j, i = 1, \cdots, k$. In this article, we develop constrained $100P^\ast{\tt\%}$ two-sided simultaneous confidence intervals for $\theta_i - \max_{j \neq i} \theta_j$ which we refer to as (constrained) MCB intervals. It turns out that the lower bounds of the intervals imply Indifference Zone selection inference, and the…

区组设计中相对于最佳处理的同时推断AI 解读

In many practical situations, only inference concerning the unknown best treatment and comparisons of the individual treatments with respect to the best treatment are of primary interest. We give an explicit formulation of this problem in the framework of randomized complete block designs. A procedure based on sample means and two procedures based on signed ranks are proposed. It is seen that, for the problem considered, these procedures have a substantial advantage over…

所有对象相对“最优者”差距的同时置信区间AI 解读

In practice, comparisons with the "best" are often the ones of primary interest. In this paper, parametric and nonparametric simultaneous upper confidence intervals for all distances from the "best" are derived under the location model. Their improvement upon the results of Bechhofer (1954), Gupta (1956, 1965), Fabian (1962), and Desu (1970) in the parametric case is discussed. In the nonparametric case, no comparable confidence statements were available previously.