Sparse matrices, and the estimation of variance components by likelihood methods

Autor:	William H. Fellner
Rok vydání:	1987
Předmět:	Statistics and Probability Mixed model Analysis of covariance Reduction (complexity) Factorial Mathematical optimization Modeling and Simulation Statistics Convergence (routing) Factorial experiment Covariance Sparse matrix Mathematics
Zdroj:	Communications in Statistics - Simulation and Computation. 16:439-463
ISSN:	1532-4141 0361-0918
DOI:	10.1080/03610918708812599
Popis:	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.
Databáze:	OpenAIRE
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