A FAST SCORING ALGORITHM FOR MAXIMUM-LIKELIHOOD-ESTIMATION IN UNBALANCED MIXED MODELS WITH NESTED RANDOM EFFECTS
成果类型：
Article
署名作者：
LONGFORD, NT
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
发表日期：
1987
页码：
817827
关键词：
摘要：
A fast Fisher scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects is described. The algorithm uses explicit formulae for the inverse and the determinant of the covariance matrix, given by LaMotte (1972), and avoids inversion of large matrices. Description of the algorithm concentrates on computational aspects for large sets of data.