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阅读说明

本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。

DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。

引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。

关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。

分类依据与纠错

AI 根据所提供的论文材料归类:摘要研究通用统计决策中的层级组检验,组结构不构成面板、网络干扰或金融风险的直接证据。

反馈分类问题

正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Steven F. Arnold

广义组检验程序

研究问题
当个体与分组总和都易于测量时,如何设计逐层筛查规则,在遗漏目标观测的预期损失与预期测量次数之间权衡?
方法与思路
作者在独立同分布且密度已知的设置下研究层级组和测量,以遗漏损失及测量次数的加权和定义并刻画最优规则类别。
材料陈述的贡献
摘要称所构造规则类别刻画加权目标的最优决策,其并集构成基本完备类;单调损失可简化规则,并给出正态情形计算。
阅读关注点
应核对层级划分、已知密度和测量成本设定,以及最优规则的递推计算、基本完备性的含义与单调损失下的简化。
阅读边界
仅据版本未注明的 OpenAlex 摘要;结论依赖所设损失与测量框架,不能推至密度未知或测量含误差的情形,刊本变化未知。

原文摘要

A person wishes to determine which, if any, of $n = \Pi^k_{j=1} a_j$ i.i.d. random variables, $X(i_1,\cdots, i_k), i_j = 1,\cdots, a_j$, lie in some specified set $A$. Such observations will be called unsafe. It is assumed that the density of the $X$'s is known and that $Y_j(i_1,\cdots, i_j)$, the sum of all the $X$'s whose first $j$ indices are $i_1,\cdots, i_j$, can be measured as easily as the individual $X$'s. In this paper, search procedures of the following form are studied. The person first measures $Y_0$, the sum of all the $X$'s. On the basis of $Y_0$, he decides whether to stop, and classify all the $X$'s as safe, or to continue and measure $Y_1(1),\cdots, Y_1(a_1 - 1)$ (and hence know $Y_1(a_1) = Y_0 - \sum^{a_1-1}_{i=1} Y_1(i))$. If he has decided to continue, he measures $Y_1(j)$. For each of $(Y_0, Y_1(j))$, he must decide whether to stop and classify as safe all $X$'s whose first index is $j$, or to continue and measure $Y_2(j, 1),\cdots, Y_2(j, a_2 - 1)$ (and hence know $Y_2(j, a_2))$. He continues in this fashion until each $X$ has either been classified safe or has been observed. Unlike most group testing problems, he is not restricted to procedures that will locate all the unsafe observations. Instead there is a loss function $L(x)$ measuring the loss if $X(i_1,\cdots, i_k) = x$ and is not observed. Let $V_1$ be the expected loss of a procedure (summed over all the $X$'s), and let $V_2$ be the expected number of measurements. For each $0 \leqq p \leqq 1$, a class of rules $D(p)$ is defined such that if a procedure is in $D(p)$, it minimizes $pV_1 + (1 - p)V_2$, and conversely, if a procedure minimizes $pV_1 + (1 - p)V_2$, then there is a rule in $D(p)$ that leads to the same decisions a.e. The union of the $D(p)$ is shown to be an essentially complete class of rules. A simpler form for the rules in $D(p)$ is derived for the case where the loss function is nondecreasing. More specific calculations are given for the case where the $X$'s are normally distributed, and $L(x)$ is the indicator function for the set $\{x \geqq d\}$.

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