跳到正文
← 返回论文列表与筛选
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:摘要讨论一般重复测量的多元非参数检验,未涉及经济面板或分布效应,不能仅凭重复测量归入所列方向。

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
Show-Li Jan
Ronald H. Randles

简单重复测量设计的方向间检验

研究问题
在简单重复测量设计中,方向间符号检验和符号秩检验能否提供稳健推断,其功效如何受尾部与协方差结构影响?
方法与思路
作者把单样本方向间符号及符号秩检验用于重复测量,分析小样本性质和渐近分布,并以相对效率与模拟比较。
材料陈述的贡献
摘要称符号检验具有小样本分布无关性质,并报告两类检验的渐近结果及在不同尾部和协方差情形下的性能差异。
阅读关注点
应核对分布无关性质的条件、椭圆对称与局部备择要求,以及重尾或偏斜模拟和不同协方差结构的具体设置。
阅读边界
仅据版本未注明的 OpenAlex 索引摘要;极限分布符号的排版不清,需查原 PDF;稳健性比较也不能脱离所述条件。

原文摘要

Interdirection tests are proposed for a simple repeated-measures design. The test statistics proposed are applications of the one-sample interdirection sign test and interdirection signed-rank test to a repeated-measurement setting. The interdirection sign test has a small-sample distribution-free property and includes the two-sided univariate sign test and Blumen's bivariate sign test as special cases. The interdirection signed-rank test includes the two-sided univariate Wilcoxon signed-rank test as a special case. The proposed statistics are shown to have a limiting X p−1 2 null distribution when the underlying distribution is elliptically symmetric. In addition, the asymptotic distributions of the proposed statistics under certain contiguous alternatives are obtained for elliptically symmetric distributions with a particular density function form. Pitman asymptotic relative efficiencies and Monte Carlo studies show the proposed interdirection tests to be robust as compared to several competitors. The sign test performs particularly well when the underlying distribution is heavy tailed or skewed, especially for non—H-type variance—covariance. For normal to light-tailed distributions, Hotelling's T 2 test and the signed-rank test have good powers when the variance—covariance structure of the underlying distribution is non—H-type; otherwise analysis of variance (ANOVA) F and the rank transformation test RT perform well.

笔记与分组
相关领域论文