Lee S Y, Leung K M
Department of Statistics, Chinese University of Hong Kong, Shatin.
Br J Math Stat Psychol. 1992 Nov;45 ( Pt 2):225-38. doi: 10.1111/j.2044-8317.1992.tb00989.x.
The main purpose of this paper is to investigate various approaches in analysing the multivariate polychoric and polyserial correlation model in the presence of incomplete data. For the general case with missing entries in both continuous and polytomous variables, a pseudo maximum likelihood method, and a partition pseudo maximum likelihood are developed. Iterative procedures based on the Fletcher-Powell algorithm and the Newton-Raphson algorithm are implemented to obtain various solutions. For the special case with missing entries only in the polytomous variables, a full maximum likelihood estimate is obtained with the help of an appropriate one-one onto transformation that significantly simplifies the computational burden. The analogous approaches as in the general case are also investigated. Finally, a simulation study is conducted to compare the performances of the various approaches.
本文的主要目的是研究在存在缺失数据的情况下分析多元多正态相关和多系列相关模型的各种方法。对于连续变量和多分类变量中都存在缺失值的一般情况，开发了一种伪最大似然法和一种分区伪最大似然法。基于弗莱彻 - 鲍威尔算法和牛顿 - 拉夫森算法的迭代程序被用于获得各种解决方案。对于仅在多分类变量中存在缺失值的特殊情况，借助适当的一一对应变换获得了完全最大似然估计，这显著简化了计算负担。还研究了与一般情况类似的方法。最后，进行了一项模拟研究以比较各种方法的性能。
