Empirical likelihood for the analysis of experimental designs
Empirical likelihood for the analysis of experimental designs
复制标题
DOI：
10.1080/10485252.2023.2206919
复制
发表时间：
2021-12
影响因子：
1.2
通讯作者：
Eunseop Kim;S. MacEachern;M. Peruggia
中科院分区：
文献类型：
--
作者：
Eunseop Kim;S. MacEachern;M. Peruggia
经验似然可以实现非参数的、似然驱动的推理风格，而不需要在参数模型中常规地进行限制性假设。我们开发了一个框架，将经验似然应用于实验设计的分析，解决了由阻塞和多假设检验产生的问题。除了流行的设计，如平衡的不完全块设计，我们的方法允许高度不平衡，不完全块设计。我们推导了一组经验似然检验统计量的渐近多元卡方分布，并提出了两种单步多重检验方法：渐近蒙特卡罗法和非参数自举法。这两种方法都渐进地控制了广义家庭的错误率，并有效地构建了兴趣比较的同时置信区间，而无需明确考虑潜在的协方差结构。仿真研究表明，该方法对线性混合模型的标准假设违反具有较强的鲁棒性。我们还提出了一种应用于农药实验的方法。
Empirical likelihood enables a nonparametric, likelihood-driven style of inference without restrictive assumptions routinely made in parametric models. We develop a framework for applying empirical likelihood to the analysis of experimental designs, addressing issues that arise from blocking and multiple hypothesis testing. In addition to popular designs such as balanced incomplete block designs, our approach allows for highly unbalanced, incomplete block designs. We derive an asymptotic multivariate chi-square distribution for a set of empirical likelihood test statistics and propose two single-step multiple testing procedures: asymptotic Monte Carlo and nonparametric bootstrap. Both procedures asymptotically control the generalised family-wise error rate and efficiently construct simultaneous confidence intervals for comparisons of interest without explicitly considering the underlying covariance structure. A simulation study demonstrates that the performance of the procedures is robust to violations of standard assumptions of linear mixed models. We also present an application to experiments on a pesticide.