基于个体协变量的潜在类模型拟合的Fisher评分算法
An efficient Fisher-scoring algorithm for fitting latent class models with individual covariates
摘要：
对于类权重依赖于个体协变量的潜在类模型，我们推导出一个简单的表达式，用于计算得分向量和一个方便的混合之间的观察和预期的信息矩阵，它总是正定的。这些成分，结合基于线搜索的最大化算法，提供了一个有效的工具，极大似然估计。特别是，所提出的算法是这样的，对数似然从一个步骤到下一个减少和选择的起始值是不是至关重要的达到局部最大值。我们展示了如何使用相同的算法进行数值调查模型的误指定的效果。并以教育传输为例进行了说明。
英文摘要：
For latent class models where the class weights depend on individual covariates, we derive a simple expression for computing the score vector and a convenient hybrid between the observed and the expected information matrices which is always positive defnite. These ingredients, combined with a maximization algorithm based on line search, provides an efficient tool for maximum likelihood estimation. In particular, the proposed algorithm is such that the log-likelihood never decreases from one step to the next and the choice of starting values is not crucial for reaching a local maximum. We show how the same algorithm may be used for numerical investigation of the effect of model mispecifications. An application to education transmission is used as an illustration.
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