The DOI matches its Crossref record; 84 of the 86 references it cites were found in a registry or an index.
Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
Abstract
Recent developments promise to increase greatly the popularity of maximum likelihood (ml) as a technique for estimating variance components. Patterson and Thompson (1971) proposed a restricted maximum likelihood (reml) approach which takes into account the loss in degrees of freedom resulting from estimating fixed effects. Miller (1973) developed a satisfactory asymptotic theory for ml estimators of variance components. There are many iterative algorithms that can be considered for computing the ml or reml estimates. The computations on each iteration of these algorithms are those associated with computing estimates of fixed and random effects for given values of the variance components.
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“It is interesting to note that the ReML objective function and the variational energy are formally identical under this model [15],[18].”
From Hierarchical Models in the Brain (2008) · cites this paper as [18]
In the citing paper
“We have discussed static cases of this fixed-form scheme previously and how it reduces to expectation maximisation (EM; [17]) and restricted maximum likelihood (ReML; [18]) for linear models [15].”
From Hierarchical Models in the Brain (2008) · cites this paper as [18]
Citations
Cites
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
A method for the solution of certain non-linear problems in least squares
Levenberg, K. (1944) · Quarterly of Applied Mathematics · doi:10.1090/qam/10666 · Cited by 12,753
Linear Statistical Inference and its Applications
Rao, C. R. (1973) · Wiley series in probability and statistics · doi:10.1002/9780470316436 · Cited by 10,474
Journal of The American Statistical Association
Fienberg, S. E. (2004) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess1317 · Cited by 6,289
Recovery of Inter-Block Information when Block Sizes are Unequal
Patterson, H. D., & Thompson, R. (1971) · Biometrika · doi:10.1093/biomet/58.3.545 · Cited by 4,073
Searle, S. R., & Searle, S. R. (1971) · Cited by 3,229
Best Linear Unbiased Estimation and Prediction under a Selection Model
Henderson, C. (1975) · Biometrics · doi:10.2307/2529430 · Cited by 2,513
Nonlinear Parameter Estimation
Chambers, J. M., & Bard, Y. (1975) · Technometrics · doi:10.2307/1268363 · Cited by 2,171
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
Journal of the Royal Statistical Society.
(1921) · The Economic Journal · doi:10.1093/ej/31.121.133 · Cited by 1,503
Research works printed but not linked: 12
- Anderson , T. W. 1969 . “Statistical Inference for Covariance Matrices with Linear Structure” . In Multivariate Analysis-II , Edited by: Krishnaiah , P. R. 55 – 66 . New York : Academic Press .
- Box , George E. P. and Jenkins , Gwilym M. 1970 . Time Series Analysis , San Francisco : Holden-Day .
- 10.1080/00401706.1976.10489397 · DOI
- Harville , David A. 1969b . “Variance-Component Estimation for the Unbalanced One-Way Random Classification-A Critique” , Ohio : Aerospace Research Laboratories, Wright-Patterson AFB . Technical Report No. 69–0180
- Harville , David A. 1975 . “Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems” , Ohio : Aerospace Research Laboratories, Wright-Patterson AFB . Technical Report No. 75–0175
- Henderson , Charles R. 1973a . “Maximum Likelihood Estimation of Variance Components” unpublished manuscript
- Henderson , Charles R. 1973b . “MINQUE of Variance Components” unpublished manuscript
- 10.1080/00401706.1976.10489395 · DOI
- LaMotte , Lynn R. 1970 . “A Class of Estimators of Variance Components” , Lexington , Kentucky : Department of Statistics, University of Kentucky . Technical Report No. 10
- LaMotte , Lynn R. 1971 . “Locally Best Quadratic Estimators of Variance Components” , Lexington , Kentucky : Department of Statistics, University of Kentucky . Technical Report No. 22
- Proschan , F. and Serfling , R. J. 1974 . “Prediction, Mixed Models, and Variance Components” . In Reliability and Biometry , Edited by: Proschan , F. and Serfling , R. J. Ithaca , New York : Biometrics Unit, Cornell University . Paper No. BU-586-M
- Townsend , Edwin C. 1968 . “Unbiased Estimators of Variance Components in Simple Unbalanced Designs” , Ph.D. dissertation Ithaca , New York : Cornell University .
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Paul, K. I., Roxburgh, S. H., England, J. R., de Ligt, R., Larmour, J. S., Brooksbank, K., Murphy, S., Ritson, P., Hobbs, T. J., Lewis, T., et al. (2014) · Forest Ecology and Management · doi:10.1016/j.foreco.2014.11.025 · Cited by 50
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Zimmerman, D. L. (1989) · Mathematical Geology · doi:10.1007/bf00893314 · Cited by 50
Metadata
- Authors
- Harville, D. A.
- Affiliations
- Iowa State University (US)
- Corresponding author
- Harville, David A.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1977-06
- Volume
- 72
- Issue
- 358
- Pages
- 320-338
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1982585616
- Cited by
- 2,667
- References
- 86
- Citations by year
- 2026: 56 · 2025: 62 · 2024: 72 · 2023: 86 · 2022: 113 · 2021: 73 · 2020: 71 · 2019: 77 · 2018: 75 · 2017: 79 · 2016: 69 · 2015: 76 · 2014: 114 · 2013: 70 · 2012: 109
- FWCI
- 16.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Defense Technical Information Center (DTIC) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Keywords
- variance component estimation, maximum likelihood estimation, restricted maximum likelihood, fixed and random effects, iterative methods, asymptotic theory, mixed models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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