Mean Squared Error of Estimation or Prediction under a General Linear Model
作者:
DavidA. Harville,
DanielR. Jeske,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1992)
卷期:
Volume 87,
issue 419
页码: 724-731
ISSN:0162-1459
年代: 1992
DOI:10.1080/01621459.1992.10475274
出版商: Taylor & Francis Group
关键词: Best linear unbiased estimation;Best linear unbiased prediction;Empirical Bayes inference;Incomplete block designs;Mixed linear model;Small-area estimation
数据来源: Taylor
摘要:
The problem considered is that of predicting a linear combination of the fixed and random effects of a mixed-effects linear model. More generally, the problem considered is that of predicting an unobservable random variable from a set of observable random variables. The best linear-unbiased predictor depends on parameters which generally are unknown. Various exact or approximate expressions are given for the mean squared error (MSE) of the predictor obtained by replacing the unknown parameters with estimates. Several estimators of the MSE are investigated.
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