Likelihood Factorizations for Mixed Discrete and Continuous Variables
Likelihood Factorizations for Mixed Discrete and Continuous Variables
复制标题
混合离散和连续变量的似然因式分解
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
N. Wermuth
中科院分区：
文献类型：
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作者：
David R. Cox;N. Wermuth
对似然因子分解、基于参数的区分因子分解和集中图因子分解做了一些一般性的评论。讨论了离散和连续混合变量的两个参数分布族。条件图的情况下，他们的联合分析可以分为单独的分析，每一个涉及一组减少的组件变量和参数。结果表明，尽管两类图都涉及相同的素图必要条件，但它们之间存在着显著的差异。这一条件对于高斯模型和离散对数线性模型的简化估计是充分必要的。
Some general remarks are made about likelihood factorizations, distinguishing parameter‐based factorizations and concentration‐graph factorizations. Two parametric families of distributions for mixed discrete and continuous variables are discussed. Conditions on graphs are given for the circumstances under which their joint analysis can be split into separate analyses, each involving a reduced set of component variables and parameters. The result shows marked differences between the two families although both involve the same necessary condition on prime graphs. This condition is both necessary and sufficient for simplified estimation in Gaussian and for discrete log linear models.