The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Unbiasedness of two-stage estimation and prediction procedures for mixed linear models
Abstract
The traditional method for estimating or predicting linear combinations of the fixed effects and realized values of the random effects in mixed linear models is first to estimate the variance components and then to proceed as if the estimated values of the variance components were the true values. This two-stage procedure gives unbiased estimators or predictors of the linear combinations provided the data vector is symmetrically distributed about its expected value and provided the variance component estimators are translation-invariant and are even functions of the data vector. The standard procedures for estimating the variance components yield even, translation-invariant estimators.
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Citations
Cites
Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
Harville, D. A. (1977) · Journal of the American Statistical Association · doi:10.1080/01621459.1977.10480998 · Cited by 2,667
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Conducting Meta-Analyses in R with the metafor Package
Viechtbauer, W. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i03 · Cited by 20,464
Metadata
- Authors
- Kackar, R. N., & Harville, D. A.
- Venue
- Communications in Statistics - Theory and Methods
- Publisher
- Informa UK Limited
- Published
- 1981-01
- Volume
- 10
- Issue
- 13
- Pages
- 1249-1261
- ISSN
- 0361-0926 (ISSN-L)
- 1532-415X
- OpenAlex
- W2075588483
- Cited by
- 159
- References
- 1
- FWCI
- 2.38
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kackar, R. N., & Harville, D. A. (1981). Unbiasedness of two-stage estimation and prediction procedures for mixed linear models. Communications in Statistics - Theory and Methods. https://doi.org/10.1080/03610928108828108
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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