Volume 55, Issue 1

Maximum Likelihood Analysis for Heteroscedastic One‐Way Random Effects ANOVA in Interlaboratory Studies

Mark G. Vangel

Statistical Engineering Division, National Institute of Standards and Technology, Building 820, Room 353, 820 West Diamond Avenue, Gaithersburg, Maryland 20899‐0001, U.S.A. email:vangel@cam.nist.gov

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Andrew L. Rukhin

Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, Maryland 21228‐5398, U.S.A.

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First published: 26 May 2004
Citations: 40

Abstract

Summary. This article presents results for the maximum likelihood analysis of several groups of measurements made on the same quantity. Following Cochran (1937, Journal of the Royal Statistical Society4(Supple), 102–118; 1954, Biometrics10, 101–129; 1980, in Proceedings of the 25th Conference on the Design of Experiments in Army Research, Development and Testing, 21–33) and others, this problem is formulated as a one‐way unbalanced random‐effects ANOVA with unequal within‐group variances. A reparametrization of the likelihood leads to simplified computations, easier identification and interpretation of multimodality of the likelihood, and (through a non‐informative‐prior Bayesian approach) approximate confidence regions for the mean and between‐group variance.

Number of times cited according to CrossRef: 40

  • Exact inference on the random‐effects model for meta‐analyses with few studies, Biometrics, 10.1111/biom.12998, 75, 2, (485-493), (2019).
  • Statistics of Interlaboratory In Vitro Toxicological Studies , Alternatives to Laboratory Animals, 10.1177/026119290303101s03, 31, 1_suppl, (43-63), (2019).
  • Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950–Metabolites in Frozen Human Plasma, Journal of Lipid Research, 10.1194/jlr.M079012, 58, 12, (2275-2288), (2017).
  • Consensus building for interlaboratory studies, key comparisons, and meta-analysis, Metrologia, 10.1088/1681-7575/aa6c0e, 54, 3, (S34-S62), (2017).
  • Interleukin-13 +2044 G/A and +1923C/T polymorphisms are associated with asthma susceptibility in Asians, Medicine, 10.1097/MD.0000000000009203, 96, 51, (e9203), (2017).
  • Meta-Analysis Options for Inconsistent Nuclear Measurements, Nuclear Science and Engineering, 10.13182/NSE11-112, 173, 1, (15-27), (2017).
  • Pseudo-Likelihoods for Bayesian Inference, Topics on Methodological and Applied Statistical Inference, 10.1007/978-3-319-44093-4, (205-220), (2016).
  • Integrated Likelihood Inference in Small Sample Meta‐analysis for Continuous Outcomes, Scandinavian Journal of Statistics, 10.1111/sjos.12172, 43, 1, (191-201), (2015).
  • A Comparison of Bayesian Models of Heteroscedasticity in Nested Normal Data, Communications in Statistics - Simulation and Computation, 10.1080/03610918.2014.936467, 45, 8, (2947-2964), (2015).
  • Confidence Regions and Intervals for Meta-Analysis Model Parameters, Technometrics, 10.1080/00401706.2014.962707, 57, 4, (547-558), (2015).
  • A Parametric Bootstrap Test for Two-Way ANOVA Model Without Interaction Under Heteroscedasticity, Communications in Statistics - Simulation and Computation, 10.1080/03610918.2013.818689, 44, 5, (1264-1272), (2014).
  • Quantitative assessment of the association between miR-196a2 rs11614913 polymorphism and cancer risk: evidence based on 45,816 subjects, Tumor Biology, 10.1007/s13277-014-1822-3, 35, 7, (6271-6282), (2014).
  • Interlaboratory Comparisons, Wiley StatsRef: Statistics Reference Online, 10.1002/9781118445112, (2014).
  • Interlaboratory Studies, Wiley StatsRef: Statistics Reference Online, 10.1002/9781118445112, (2014).
  • XRCC1 polymorphisms and differentiated thyroid carcinoma risk: A meta-analysis, Gene, 10.1016/j.gene.2013.07.005, 528, 2, (67-73), (2013).
  • Analysis of key comparison data: critical assessment of elements of current practice with suggested improvements, Metrologia, 10.1088/0026-1394/50/5/549, 50, 5, (549-555), (2013).
  • Results of an international comparison for the activity measurement of 177Lu, Applied Radiation and Isotopes, 10.1016/j.apradiso.2012.02.014, 70, 9, (1825-1830), (2012).
  • A note on fiducial generalized pivots for in one-way heteroscedastic ANOVA with random effects, Statistics, 10.1080/02331888.2010.540669, 46, 4, (489-504), (2011).
  • Higher order inference for the consensus mean in inter‐laboratory studies, Biometrical Journal, 10.1002/bimj.201000032, 53, 1, (128-136), (2010).
  • On multiple-method studies, Metrologia, 10.1088/0026-1394/47/6/002, 47, 6, (642-645), (2010).
  • Non-negative estimation of variance components in heteroscedastic one-way random-effects ANOVA models, Statistics, 10.1080/02331880903237106, 44, 6, (557-569), (2010).
  • Weighted means statistics in interlaboratory studies, Metrologia, 10.1088/0026-1394/46/3/021, 46, 3, (323-331), (2009).
  • Bibliography, Linear Model Methodology, 10.1201/9781420010442, (515-534), (2009).
  • Robust testing for random effects in unbalanced heteroscedastic one-way models, Journal of Nonparametric Statistics, 10.1080/10485250802018477, 20, 4, (305-317), (2008).
  • Searching a robust reference value from intercomparison exercise data: Seaweed radionuclide Standard Reference Material (SRM4359), Journal of Radioanalytical and Nuclear Chemistry, 10.1007/s10967-008-0507-z, 276, 2, (329-334), (2008).
  • Interlaboratory Comparisons, Encyclopedia of Statistics in Quality and Reliability, 10.1002/9780470061572, (2008).
  • Multiple-humped fission and fusion barriers of actinide and superheavy elements, Journal of Radioanalytical and Nuclear Chemistry, 10.1007/s10967-007-0507-4, 272, 2, (237-242), (2007).
  • Estimating common vector parameters in interlaboratory studies, Journal of Multivariate Analysis, 10.1016/j.jmva.2006.09.005, 98, 3, (435-454), (2007).
  • Bayesian Approaches to Calculating a Reference Value in Key Comparison Experiments, Technometrics, 10.1198/004017006000000273, 49, 1, (81-87), (2007).
  • Adaptive estimation of error density in nonparametric regression with small sample size, Journal of Statistical Planning and Inference, 10.1016/j.jspi.2006.01.005, 137, 2, (363-378), (2007).
  • A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models, Computational Statistics & Data Analysis, 10.1016/j.csda.2006.09.039, 51, 12, (5731-5742), (2007).
  • Estimation in Two‐Stage Models with Heteroscedasticity, International Statistical Review, 10.1111/j.1751-5823.2006.tb00303.x, 74, 3, (403-418), (2007).
  • Combining Data in Small Multiple-Method Studies, Technometrics, 10.1198/004017005000000300, 48, 2, (293-302), (2006).
  • Meaning and models in key comparisons, with measures of operability and interoperability, Metrologia, 10.1088/0026-1394/43/4/S08, 43, 4, (S220-S230), (2006).
  • Interlaboratory Studies, Encyclopedia of Environmetrics, 10.1002/9780470057339, (2006).
  • One-Way Classification, Analysis of Variance for Random Models, 10.1007/b138864, (93-163), (2005).
  • Combining Functional MRI Data on Multiple Subjects, Classification, Clustering, and Data Mining Applications, 10.1007/978-3-642-17103-1, (469-476), (2004).
  • Ch. 22. Repeatability, reproducibility and interlaboratory studies, Statistics in Industry, 10.1016/S0169-7161(03)22024-1, (795-822), (2003).
  • Statistical determination of a comparison reference value using hidden errors, Metrologia, 10.1088/0026-1394/39/4/3, 39, 4, (343-354), (2003).
  • Analysis of key comparison data: assessment of current methods for determining a reference value, Measurement Science and Technology, 10.1088/0957-0233/12/9/308, 12, 9, (1431-1438), (2001).

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