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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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 .
Cited by
Sorted by citations
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
“It can be shown that the estimates Of u2, 3l *,s ak+1 found in this way are identical to those described by Patterson and Thompson (1971), Corbeil and Searle (1976) and Harville (1977) under the label REML, or "restricted" maximum likelihood.”
ResultMeta-analysis in clinical trials
DerSimonian, R., & Laird, N. (1986) · Controlled Clinical Trials · doi:10.1016/0197-2456(86)90046-2 · Cited by 39,552
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
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Longitudinal Data Analysis for Discrete and Continuous Outcomes
Zeger, S. L., & Liang, K. (1986) · Biometrics · doi:10.2307/2531248 · Cited by 7,781
Wood, S. N. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2010.00749.x · Cited by 7,538
Approximate Inference in Generalized Linear Mixed Models
Breslow, N. E., & Clayton, D. G. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594284 · Cited by 4,662
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583
Small sample inference for fixed effects from restricted maximum likelihood
Kenward, M. G., & Roger, J. H. (1997) · Biometrics · doi:10.2307/2533558 · Cited by 4,552
Improved allometric models to estimate the aboveground biomass of tropical trees
Chave, J., Réjou‐Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B. C., Duque, A., Eid, T., Fearnside, P. M., Goodman, R. C., et al. (2014) · Global Change Biology · doi:10.1111/gcb.12629 · Cited by 3,131
An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units
Moulton, B. R. (1990) · The Review of Economics and Statistics · doi:10.2307/2109724 · Cited by 2,567
Random-effects model for meta-analysis of clinical trials: An update
DerSimonian, R., & Kacker, R. (2007) · Contemporary Clinical Trials · doi:10.1016/j.cct.2006.04.004 · Cited by 2,374
Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples
Betoule, M., Kessler, R., Guy, J., Mosher, J., Hardin, D., Biswas, R., Astier, P., El-Hage, P., König, M., Kuhlmann, S. E., et al. (2014) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201423413 · Cited by 1,910
Bias and Efficiency of Meta-Analytic Variance Estimators in the Random-Effects Model
Viechtbauer, W. (2005) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986030003261 · Cited by 1,699
Timm, N. H. (2004) · Springer eBooks · doi:10.1007/b98963 · Cited by 1,664
Random group effects and the precision of regression estimates
Moulton, B. R. (1986) · Journal of Econometrics · doi:10.1016/0304-4076(86)90021-7 · Cited by 1,574
Application of hierarchical linear models to assessing change.
Bryk, A. S., & Raudenbush, S. W. (1987) · Psychological Bulletin · doi:10.1037/0033-2909.101.1.147 · Cited by 1,505
Modeling Multilevel Data Structures
Steenbergen, M. R., & Jones, B. (2002) · American Journal of Political Science · doi:10.2307/3088424 · Cited by 1,521
A free energy principle for the brain
Friston, K., Kilner, J., & Harrison, L. (2006) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2006.10.001 · Cited by 1,369
Cheung, M. W.-L. (2014) · Psychological Methods · doi:10.1037/a0032968 · Cited by 1,342
Meta-Analysis in Clinical Research
L'ABBÉ, K. A., DETSKY, A. S., & O'ROURKE, K. (1987) · Annals of Internal Medicine · doi:10.7326/0003-4819-107-2-224 · Cited by 1,267
Generalized linear mixed models a pseudo-likelihood approach
Wolfinger, R. D., & O'Connell, M. (1993) · Journal of Statistical Computation and Simulation · doi:10.1080/00949659308811554 · Cited by 1,257
Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide
Twisk, J. W. R. (2003) · Cited by 1,244
Estimation in Generalized Linear Models with Random Effects
Schall, R. (1991) · Biometrika · doi:10.1093/biomet/78.4.719 · Cited by 1,230
Approximations to the Log-Likelihood Function in the Nonlinear Mixed-Effects Model
Pinheiro, J. C., Bates, D. M., & Pinheiro, J. C. (1995) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1995.10474663 · Cited by 1,171
Reducing and meta-analysing estimates from distributed lag non-linear models
Gasparrini, A., & Armstrong, B. (2013) · BMC Medical Research Methodology · doi:10.1186/1471-2288-13-1 · Cited by 1,081
A comparison of heterogeneity variance estimators in simulated random‐effects meta‐analyses
Langan, D., Higgins, J. P. T., Jackson, D., Bowden, J., Veroniki, A. A., Kontopantelis, E., Viechtbauer, W., & Simmonds, M. (2019) · Research Synthesis Methods · doi:10.1002/jrsm.1316 · Cited by 1,074
The vaginal microbiome and preterm birth
Fettweis, J. M., Serrano, M. G., Brooks, J. P., Edwards, D. J., Girerd, P. H., Parikh, H. I., Huang, B., Arodz, T. J., Edupuganti, L., Glascock, A. L., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0450-2 · Cited by 983
Hierarchical Generalized Linear Models
Lee, Y., & Nelder, J. A. (1996) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1996.tb02105.x · Cited by 958
Hierarchical Models in the Brain
Friston, K. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000211 · Cited by 952
“It is interesting to note that the ReML objective function and the variational energy are formally identical under this model [15,18].”
BackgroundMethodAn Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem
Wipf, D., & Rao, B. D. (2007) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.894265 · Cited by 912
Harbord, R. M., & Higgins, J. P. T. (2008) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0800800403 · Cited by 897
On Deriving the Inverse of a Sum of Matrices
Henderson, H. V., & Searle, S. R. (1981) · SIAM Review · doi:10.1137/1023004 · Cited by 883
Variational free energy and the Laplace approximation
Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J., & Penny, W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.08.035 · Cited by 880
Frees, E. W. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511790928 · Cited by 872
Chamberlain, G. (1984) · Handbook of econometrics · doi:10.1016/s1573-4412(84)02014-6 · Cited by 834
variancePartition: interpreting drivers of variation in complex gene expression studies
Hoffman, G. E., & Schadt, E. E. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1323-z · Cited by 769
A Hierarchical Model for Studying School Effects
Raudenbush, S. W., & Bryk, A. S. (1986) · Sociology of Education · doi:10.2307/2112482 · Cited by 812
Multilevel Mixed Linear Model Analysis Using Iterative Generalized Least Squares
Goldstein, H. (1986) · Biometrika · doi:10.1093/biomet/73.1.43 · Cited by 798
An Error-Components Model for Prediction of County Crop Areas Using Survey and Satellite Data
Battese, G. E., Harter, R., & Fuller, W. A. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478561 · Cited by 789
Multilevel Modeling for Binary Data
Guo, G., & Zhao, H. J. (2000) · Annual Review of Sociology · doi:10.1146/annurev.soc.26.1.441 · Cited by 785
Multivariate meta-analysis for non-linear and other multi-parameter associations
Gasparrini, A., Armstrong, B., & Kenward, M. G. (2012) · Statistics in Medicine · doi:10.1002/sim.5471 · Cited by 772
The Estimation of the Mean Squared Error of Small-Area Estimators
Prasad, N., & Rao, J. N. K. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10475320 · Cited by 766
Covariance structure selection in general mixed models
Wolfinger, R. D. (1993) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610919308813143 · Cited by 760
Opinion Leaders vs Audit and Feedback to Implement Practice Guidelines
Lomas, J. (1991) · JAMA · doi:10.1001/jama.265.17.2202 · Cited by 744
Random-Effects Models for Serial Observations with Binary Response
Stiratelli, R. G., Laird, N. M., & Ware, J. H. (1984) · Biometrics · doi:10.2307/2531147 · Cited by 733
Davidian, M., & Carroll, R. J. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478543 · Cited by 678
Design and analysis of group-randomized trials: a review of recent methodological developments
Murray, D. M., Varnell, S. P., & Blitstein, J. L. (2004) · American Journal of Public Health · doi:10.2105/ajph.94.3.423 · Cited by 677
Friston, K. J., & Stephan, K. E. (2007) · Synthese · doi:10.1007/s11229-007-9237-y · Cited by 673
The ECME Algorithm: A Simple Extension of EM and ECM with Faster Monotone Convergence
Liu, C., & Rubin, D. B. (1994) · Biometrika · doi:10.1093/biomet/81.4.633 · Cited by 607
Classical and Bayesian Inference in Neuroimaging: Theory
Friston, K. J., Penny, W. D., Phillips, C. L. M., Kiebel, S. J., Hinton, G. E., & Ashburner, J. (2002) · NeuroImage · doi:10.1006/nimg.2002.1090 · Cited by 597
Multiple sparse priors for the M/EEG inverse problem.
Friston, K., Harrison, L., Daunizeau, J., Kiebel, S., Phillips, C., Trujillo-Barreto, N., Henson, R., Flandin, G., & Mattout, J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.048 · Cited by 589
A Unified Approach to Mixed Linear Models
McLean, R. A., Sanders, W. L., & Stroup, W. W. (1991) · The American Statistician · doi:10.1080/00031305.1991.10475767 · Cited by 588
Testing panel data regression models with spatial error correlation
Baltagi, B. H., Song, S. H., & Koh, W. (2003) · Journal of Econometrics · doi:10.1016/s0304-4076(03)00120-9 · Cited by 586
Conditional Akaike information for mixed-effects models
Vaida, F., & Blanchard, S. (2005) · Biometrika · doi:10.1093/biomet/92.2.351 · Cited by 579
Software Selegen-REML/BLUP: a useful tool for plant breeding
de Resende, M. D. V. (2016) · Crop Breeding and Applied Biotechnology · doi:10.1590/1984-70332016v16n4a49 · Cited by 575
On Using Bayesian Methods to Address Small Sample Problems
McNeish, D. (2016) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2016.1186549 · Cited by 612
A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models
Chung, Y., Rabe‐Hesketh, S., Dorie, V., Gelman, A., & Liu, J. (2013) · Psychometrika · doi:10.1007/s11336-013-9328-2 · Cited by 598
Guy, J., Sullivan, M., Conley, A., Regnault, N., Astier, P., Balland, C., Basa, S., Carlberg, R. G., Fouchez, D., Hardin, D., et al. (2010) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201014468 · Cited by 551
Measuring explained variation in linear mixed effects models
Xu, R. (2003) · Statistics in Medicine · doi:10.1002/sim.1572 · Cited by 537
Screening for Obesity and Intervention for Weight Management in Children and Adolescents
O’Connor, E., Evans, C. V., Burda, B. U., Walsh, E. S., Eder, M. L., & Lozano, P. (2017) · JAMA · doi:10.1001/jama.2017.0332 · Cited by 515
Bias Correction in Generalised Linear Mixed Models with a Single Component of Dispersion
Breslow, N. E., & LIN, X. (1995) · Biometrika · doi:10.1093/biomet/82.1.81 · Cited by 494
Faghih-Imani, A., Eluru, N., El-Geneidy, A., Rabbat, M., & Haq, U. (2014) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2014.01.013 · Cited by 485
West, B. T., Welch, K. B., & Gałecki, A. T. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/b17198 · Cited by 485
Approximations for Standard Errors of Estimators of Fixed and Random Effect in Mixed Linear Models
Kackar, R. N., & Harville, D. A. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10477102 · Cited by 484
Self-citationLinear Models for the Analysis of Longitudinal Studies
Ware, J. H. (1985) · The American Statistician · doi:10.1080/00031305.1985.10479402 · Cited by 484
Kass, R. E., & Steffey, D. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478825 · Cited by 474
An Assessment of Estimation Procedures for Multilevel Models with Binary Responses
Rodrı́guez, G., & Goldman, N. (1995) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2983404 · Cited by 472
Statistical analysis of fNIRS data: A comprehensive review
Tak, S., & Ye, J. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.016 · Cited by 493
The Hierarchical Logistic Regression Model for Multilevel Analysis
Wong, G. Y., & Mason, W. M. (1985) · Journal of the American Statistical Association · doi:10.1080/01621459.1985.10478148 · Cited by 460
Felsenstein, J. (1981) · Evolution · doi:10.1111/j.1558-5646.1981.tb04991.x · Cited by 456
Estimation in Covariance Components Models
Dempster, A. P., Rubin, D. B., & Tsutakawa, R. K. (1981) · Journal of the American Statistical Association · doi:10.1080/01621459.1981.10477653 · Cited by 444
Likelihood Ratio Tests in Linear Mixed Models with One Variance Component
Crainiceanu, C. M., & Ruppert, D. (2003) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2004.00438.x · Cited by 441
METHODS FOR THE ANALYSIS OF COMPARATIVE DATA IN EVOLUTIONARY BIOLOGY
Lynch, M. (1991) · Evolution · doi:10.1111/j.1558-5646.1991.tb04375.x · Cited by 436
A Mixed-Model Procedure for Analyzing Ordered Categorical Data
Harville, D. A., & Mee, R. W. (1984) · Biometrics · doi:10.2307/2531393 · Cited by 431
Self-citationRestricted maximum likelihood estimation of covariances in sparse linear models
Neumaier, A., & Groeneveld, E. (1998) · Genetics Selection Evolution · doi:10.1186/1297-9686-30-1-3 · Cited by 431
Meyer, K. (1989) · Génétique Sélection Évolution · doi:10.1051/gse:19890308 · Cited by 426
Hierarchical Linear Models: Applications and Data Analysis Methods
Calvin, J. A., Bryk, A. S., & Raudenbush, S. W. (1994) · Technometrics · doi:10.2307/1269211 · Cited by 422
Bias Correction in Generalized Linear Mixed Models With Multiple Components of Dispersion
Lin, X., & Breslow, N. E. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476971 · Cited by 408
Maximum Likelihood Computations with Repeated Measures: Application of the EM Algorithm
Laird, N. M., LANGE, N. D., & Stram, D. O. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478395 · Cited by 404
Using Genotype-by-Environment Interaction for Crop Cultivar Development
Kang, M. S. (1997) · Advances in agronomy · doi:10.1016/s0065-2113(08)60569-6 · Cited by 398
Heterogeneous Variance: Covariance Structures for Repeated Measures
Wolfinger, R. D. (1996) · Journal of Agricultural Biological and Environmental Statistics · doi:10.2307/1400366 · Cited by 398
Testing for serial correlation, spatial autocorrelation and random effects using panel data
Baltagi, B. H., Song, S. H., Jung, B. C., & Koh, W. (2006) · Journal of Econometrics · doi:10.1016/j.jeconom.2006.09.001 · Cited by 387
Modelling Progression of CD4-Lymphocyte Count and Its Relationship to Survival Time
Gruttola, V. D., & Tu, X. M. (1994) · Biometrics · doi:10.2307/2533439 · Cited by 384
A comparison of heterogeneity variance estimators in combining results of studies
Sidik, K., & Jonkman, J. N. (2007) · Statistics in Medicine · doi:10.1002/sim.2688 · Cited by 380
Mis-Specification Analysis of Linear Degradation Models
Peng, C., & Tseng, S. (2009) · IEEE Transactions on Reliability · doi:10.1109/tr.2009.2026784 · Cited by 378
Multiple membership multiple classification (MMMC) models
Browne, W. J., Goldstein, H., & Rasbash, J. (2001) · Statistical Modelling · doi:10.1177/1471082x0100100202 · Cited by 377
Bayesian Methods in Animal Breeding Theory
Gianola, D., & Fernando, R. L. (1986) · Journal of Animal Science · doi:10.2527/jas1986.631217x · Cited by 368
Contextual Analysis through the Multilevel Linear Model
Mason, W. M., Wong, G. Y., & Entwisle, B. (1983) · Sociological Methodology · doi:10.2307/270903 · Cited by 349
Bryk, A. S., & Raudenbush, S. W. (1988) · American Journal of Education · doi:10.1086/443913 · Cited by 346
Least-squares variance component estimation
Teunissen, P. J. G., & Amiri-Simkooei, A. (2007) · Journal of Geodesy · doi:10.1007/s00190-007-0157-x · Cited by 346
Intergenerational Income Mobility and the Role of Family Background
Björklund, A., & Jäntti, M. (2011) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199606061.013.0020 · Cited by 347
McNeish, D., & Kelley, K. (2019) · Psychological Methods · doi:10.1037/met0000182 · Cited by 336
Johnson, D. L., & Thompson, R. (1995) · Journal of Dairy Science · doi:10.3168/jds.s0022-0302(95)76654-1 · Cited by 336
Diedrichsen, J., & Kriegeskorte, N. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005508 · Cited by 350
An extended mixed‐effects framework for meta‐analysis
Sera, F., Armstrong, B., Blangiardo, M., & Gasparrini, A. (2019) · Statistics in Medicine · doi:10.1002/sim.8362 · Cited by 340
Generalized linear mixed models: a review and some extensions
Dean, C. B., & Nielsen, J. D. (2007) · Lifetime Data Analysis · doi:10.1007/s10985-007-9065-x · Cited by 333
Liu, D., Lin, X., & Ghosh, D. (2007) · Biometrics · doi:10.1111/j.1541-0420.2007.00799.x · Cited by 342
Methods for Pooling Results of Epidemiologic Studies
Smith‐Warner, S. A., Spiegelman, D. L., Ritz, J., Albanes, D., Beeson, W. L., Bernstein, L., Berrino, F., van den Brandt, P. A., Buring, J. E., Cho, E., et al. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj127 · Cited by 333
ITRF2020: an augmented reference frame refining the modeling of nonlinear station motions
Altamimi, Z., Rebischung, P., Collilieux, X., Métivier, L., & Chanard, K. (2023) · Journal of Geodesy · doi:10.1007/s00190-023-01738-w · Cited by 331
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 ›
Are you an author, or do you see a problem with this page? Request a review
