Czech J. Anim. Sci., 2006, 51(6):227-235 | DOI: 10.17221/3933-CJAS

Methods of variance component estimation

D. Rasch, O. Mašata
Biometric Unit, Research Institute of Animal Production, Prague-Uhříněves, Czech Republic

Estimation of variance components is a method often used in population genetics and applied in animal breeding. Even experienced population geneticists nowadays feel lost if confronted with the huge set of different methods of variance component estimation. Especially because there exists no uniformly best method, a decision which method should be used is often difficult to take. This paper gives a complete overview of methods existing in the simplest case of a one-way lay-out and demonstrates some of them by a numerical example for which the true situation is known. Of course, the one-way lay-out is of limited practical interest but can best be used to explain animal scientists the basic principles of the methods. These basic principles are principally also valid for higher classifications. Advantages and disadvantages of the methods are discussed. The symbols used are the standard biometric symbols as given in Rasch et al. (1994). We can say that all the methods offered by SPSS can be recommended.

Keywords: one-way ANOVA; ANOVA-method; MINQUE; MIVQUE; Bayesian approach; Gibbs sampling; numerical example

Published: June 30, 2006  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Rasch D, Mašata O. Methods of variance component estimation. Czech J. Anim. Sci. 2006;51(6):227-235. doi: 10.17221/3933-CJAS.
Download citation

References

  1. Anderson R.L., Bancroft T.A. (1952): Statistical Theory in Research. McGraw-Hill, New York.
  2. Federer W.T. (1968): Non-negative estimators for components of variance. Appl. Stat., 17, 171-174. Go to original source...
  3. Fisher R.A. (1925): Statistical Methods for Research Workers, Oliver and Boyd.
  4. Gamerman D. (1997): Markov Chain Monte Carlo. Chapman and Hall, New York.
  5. Gelman A., Carlin J.B., Stern H.S., Rubin D.B. (1995): Bayesian Data Analysis. Chapman and Hall, New York. Go to original source...
  6. Herbach L.H. (1959): Properties of model II type analysis of variance tests A: Optimum nature of the F-test for model II in balanced case. Ann. Math. Statist., 30, 939-959. Go to original source...
  7. Klotz J.H., Milton R.C., Zacks S. (1969): Mean square efficiency of estimators of variance components. J. Am. Stat. Assoc., 64, 1383-1402. Go to original source...
  8. LaMotte L.R. (1973): Quadratic estimation of variance components. Biometrics, 29, 311-330. Go to original source...
  9. Rao C.R. (1971a): Estimation of variance and covariance components: MINQUE theory. J. Multivar. Anal., 1, 257-275. Go to original source...
  10. Rao C.R. (1971b): Minimum variance quadratic unbiased estimation of variance components. J. Multivar. Anal., 1, 445-456. Go to original source...
  11. Rao C.R. (1972): Estimation of variance and covariance components in linear models. J. Am. Stat. Assoc., 67, 112-115. Go to original source...
  12. Rasch D. (1995): Mathematische Statistik. Joh. Ambrosius Barth and Wiley, Berlin, Heidelberg. 851 p.
  13. Rasch D., Tiku M.L., Sumpf D. (1994): Elsevier's Dictionary of Biometry. Elsevier, Amsterdam, London, New York.
  14. Rasch D., Verdooren L.R., Gowers J.I. (1999): Fundamentals in the Design and Analysis of Experiments and Surveys - Grundlagen der Planung und Auswertung von Versuchen und Erhebungen. Oldenbourg Verlag, München, Wien.
  15. Reinsch N. (1996): Two Fortran programs for the Gibbs Sampler in univariate linear mixed models. Dummerstorf. Arch. Tierz., 39, 203-209.
  16. Sarhai H., Ojeda M.M. (2004): Analysis of Variance for Random Models, Balanced Data. Birkhäuser, Boston, Basel, Berlin. Go to original source...
  17. Sarhai H., Ojeda M.M. (2005): Analysis of Variance for Random Models, Unbalanced Data. Birkhäuser, Boston, Basel, Berlin.
  18. Sorensen A., Gianola D. (2002): Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics. Springer, New York. Go to original source...
  19. Stein C (1969): Inadmissibility of the usual estimator for the variance of a normal distribution with unknown mean. Ann. Inst. Statist. Math. (Japan), 16, 155-160. Go to original source...
  20. Tiao G.C., Tan W.Y. (1965): Bayesian analysis of random effects models in the analysis of variance I: Posterior distribution of variance components. Biometrika, 52, 37-53. Go to original source...
  21. Verdooren L.R. (1982): How large is the probability for the estimate of a variance component to be negative? Biom. J., 24, 339-360. Go to original source...

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.