The grouped continuous model for multivariate ordered categorical variables and covariate adjustment.
The grouped continuous model for multivariate ordered categorical variables and covariate adjustment.
复制标题
多元有序分类变量和协变量调整的分组连续模型。
作者：
J. Anderson;J. Pemberton
描述了多元有序分类数据的分组连续模型。这是基于对底层多元正态分布的划分。简单的最大似然估计仅适用于单向表和双向表。我们介绍了一个估计系统的基础上最大似然估计的单向和双向边际表的高阶表。这是计算上可行的，并给出了一个涉及鸟类着色方面的例子。该方法被扩展到提供一个回归模型的多元有序分类数据，估计方案再次基于单向和双向边际表。上面的例子是为了研究时间的协变量效应而开发的。这些抽样方案的渐近效率进行了讨论，看来，他们有很高的效率。
The grouped continuous model for multivariate ordered categorical data is described. This is based on partitioning an underlying multivariate normal distribution. Straightforward maximum likelihood estimation is really feasible only for one- and two-way tables. We introduce an estimation system based on maximum likelihood estimation in the one- and two-way marginal tables of higher-order tables. This is computationally feasible and an example involving aspects of bird colouring is given. The approach is extended to provide a regression model for multivariate ordered categorical data, with an estimation scheme again based on the one- and two-way marginal tables. The above example is developed to investigate the covariate effect of time. The asymptotic efficiency of these sampling schemes is discussed; it appears that they have high efficiency.