Journal of the Korean Statistical Society

The Korean Statistical Society (한국통계학회)
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A Method of Obtaning Least Squares Estimators of Estimable Functions in Classification Linear Models

  • Published : 1999.06.01
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Abstract

In the problem of estimating estimable functions in classification linear models, we propose a method of obtaining least squares estimators of estimable functions. This method is based on the hierarchical Bayesian approach for estimating a vector of unknown parameters. Also, we verify that estimators obtained by our method are identical to least squares estimators of estimable functions obtained by using either generalized inverses or full rank reparametrization of the models. Some examples are given which illustrate our results.

Keywords

References

  1. Journal of the American Statistical Association v.83 Computational methods using a Bayesian hierarchical Generalized linear model Albert, J.H.
  2. Ph. D. Thesis Department of Mathematics, Hanyang University Empirical and Hierarchical Bayes Estimators in Linear Models Chang, I.H.
  3. Journal of the American Statistical Association v.76 no.373 Information about hyperparameters in hierarchical models Goel, P.K.;DeGroot, M.H.
  4. An Introduction to Linear Statistical Models Graybill, F. A.
  5. Theory and Application of the Linear Model Graybill, F. A.
  6. Journal of the Royal Statistical Society v.34 Bayes estimates for the linear model (with discussion) Lindley, D.W.;Smith, A.F.M.
  7. Linear Statistical Inference and Its Applications Rao, C. R.
  8. The Bayesian Choice, A Decision Theoretic Motivation Robert, C. P.
  9. The Analysis of Variance Scheffe, H.
  10. Linear Models Searle, S. R.
  11. Biometrika v.60 Bayes estimates in one-way and two-way models Smith, A.F.M.