Likelihood Based Inference Using Signed Ranks for Matched Pairs
研究了基于独立匹配对内部差异的符号秩边际似然进行精确和近似推断，使用贝叶斯方法，并引入一种快速计算的近似分析，在正态分布假设下效果极佳，同时扩展到回归模型。
SUMMARY Exact and approximate inference based on the marginal likelihood which results from the signed ranks of the within pair differences of independent matched pairs is considered. Inference is made using Bayesian ideas. A quickly computed approximate analysis is introduced and this is shown to be extremely good when the within pair differences are assumed to have a normal distribution. Numerical comparisons are also made when the differences have a logistic distribution, but for this case the approximation is not as good. The approximations involve the Wilcoxon signed rank statistic and the half-normal scores signed rank statistic. The approximation is extended to consider regression models for matched pairs data. An application is given illustrating the ideas.