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AI 根据所提供的论文材料归类:摘要讨论通用高阶似然与频率学推断理论,未明确涉及所列经济学应用细分。

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关注这篇论文的作者
Donald A. Pierce
Ruggero Bellio

参照样本集合对频率学推断的影响

研究问题
研究频率学推断为何不仅依赖似然函数,还依赖数据的概率模型,并比较删失机制与序贯停止规则对高阶调整的影响。
方法与思路
作者运用二阶似然渐近理论,区分干扰参数拟合与调整后信息限制两类修正,分析与似然无关的模型设定如何改变调整阶数。
材料陈述的贡献
摘要称,一些设定对两类调整仅有二阶影响,而序贯停止规则对第二类有一阶影响;并提出基于普通似然比统计量分布的二阶观点。
阅读关注点
应核对参照集合及理想推断的定义、两类修正的精确分解,以及删失模型和停止规则结论各自依赖的正则条件与范围。
阅读边界
OpenAlex索引摘要未注明稿件版本;具体展开、条件和证明未提供,不能据此断言所有频率学推断都可省去通常的似然调整。

原文摘要

We employ second-order likelihood asymptotics to investigate how ideal frequentist inferences depend on the probability model for the data through more than the likelihood function, referring to this as the effect of the reference set. There are two aspects of higherorder corrections to first-order likelihood methods, namely (i) that involving effects of fitting nuisance parameters and leading to the modified profile likelihood, and (ii) another part pertaining to limitation in adjusted information. Generally, each of these involves a first-order adjustment depending on the reference set. However, we show that, for some important settings, likelihood-irrelevant model specifications have a second-order effect on both of these adjustments; this result includes specification of the censoring model for survival data. On the other hand, for sequential experiments the likelihood-irrelevant specification of the stopping rule has a second-order effect on adjustment (i) but a firstorder effect on adjustment (ii). These matters raise the issue of what are ‘ideal’ frequentist inferences, since consideration of ‘exact’ frequentist inferences will not suffice. We indicate that to second order ideal frequentist inferences may be based on the distribution of the ordinary likelihood ratio statistic, without commonly considered adjustments thereto.

笔记与分组