Sparse matrices, and the estimation of variance components by likelihood methods
作者:
William H. Fellner,
期刊:
Communications in Statistics - Simulation and Computation
(Taylor Available online 1987)
卷期:
Volume 16,
issue 2
页码: 439-463
ISSN:0361-0918
年代: 1987
DOI:10.1080/03610918708812599
出版商: Marcel Dekker, Inc.
关键词: resticted maximum likelihood;mixed models;analysis of covariance;algorithms
数据来源: Taylor
摘要:
It is generally considered that analysis of variance by maximum likelihood or its variants is computationally impractical, despite existing techniques for reducing computational effect per iteration and for reducing the number of iterations to convergence. This paper shows thata major reduction in the overall computational effort can be achieved through the use of sparse-matrix algorithms that take advantage of the factorial designs that characterize most applications of large analysis-of-variance problems. In this paper, an algebraic structure for factorial designsis developed. Through this structure, it is shown that the required computations can be arranged so that sparse-matrix methods result in greatly reduced storage and time requirements.
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