COMPUTING GAUSSIAN LIKELIHOODS AND THEIR DERIVATIVES FOR GENERAL LINEAR MIXED MODELS
COMPUTING GAUSSIAN LIKELIHOODS AND THEIR DERIVATIVES FOR GENERAL LINEAR MIXED MODELS
复制标题
DOI：
10.1137/0915079
复制
发表时间：
1994-11-01
影响因子：
3.1
通讯作者：
SALL, J
中科院分区：
文献类型：
--
作者：
WOLFINGER, R;TOBIAS, R;SALL, J
描述了计算一般线性混合模型的高斯似然或限制似然的算法。包括随机效应和误差的任意协方差结构。公式也给出了一阶和二阶导数的可能性，从而使牛顿-拉夫森实现。这些算法大量使用了Cholesky分解、扫描算子和W变换。还描述了方差分析、Fisher评分和MIVQUE（0）所需的修改，以及程序的计算顺序。
Algorithms are described for computing the Gaussian likelihood or restricted likelihood corresponding to a general linear mixed model. Included are arbitrary covariance structures for both the random effects and errors. Formulas are also given for the first and second derivatives of the likelihoods, thus enabling a Newton-Raphson implementation. The algorithms make heavy use of the Cholesky decomposition, the sweep operator, and the W-transformation. Also described are the modifications needed for variance profiling, Fisher scoring, and MIVQUE(0), as well as the computational order of the procedures.