Department of Statistics, University of California, 367 Evans Hall #3860, Berkeley, CA 94720-386, USA.
Stat Med. 2011 Apr 15;30(8):866-76. doi: 10.1002/sim.4157. Epub 2011 Jan 13.
The use of logistic models for independent binary data has relied first on asymptotic theory and later on exact distributions for small samples. However, the use of logistic models for dependent analysis based on exact analysis is not as common. Moreover, attention is usually given to one-stage clustering. In this paper, we extend the exact techniques to address hypothesis testing (estimation is not addressed) for data with second-stage and probably higher levels of clustering. The methods are demonstrated through a somewhat generic example using C+ + program.
基于独立二项数据的逻辑回归模型，首先依赖于渐近理论，后来又依赖于小样本的精确分布。然而，基于精确分析的相关分析中，逻辑回归模型的应用并不常见。此外，通常只关注于单级聚类。本文将扩展精确技术，以解决具有二级甚至更高层次聚类的数据的假设检验问题（未涉及估计）。通过使用 C++程序的一个通用示例来演示这些方法。
