Linear Statistical Inference Proceedings of the International Conference held at Pozna?, Poland, June 4–8, 1984
An International Statistical Conference on Linear Inference was held in Poznan, Poland, on June 4-8, 1984. The conference was organized under the auspices of the Polish Section of the Bernoulli Society, the Committee of Mathematical Sciences and the Mathematical Institute of the ,Polish Academy of S...
| Other Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
1985, 1985
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| Edition: | 1st ed. 1985 |
| Series: | Lecture Notes in Statistics
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 1. Some Geometric Tools for the Gaussian Linear Model with Applications to the Analysis of Residuals
- 2. Approximate Design Theory for a Simple Block Design with Random Block Effects
- 3. Rectangular Lattices Revisited
- 4. Multiple Comparisons between Several Treatments and a Specified Treatment
- 5. Minimax-Prediction in Linear Models
- 6. Singular Information Matrices, Directional Derivatives and Subgradients in Optimal Design Theory
- 7. A Note on Admissibility of Improved Unbiased Estimators in Two Variance Components Models
- 8. Linear Statistical Inference Based on L-Estimators
- 9. Connected Designs with the Minimum Number of Experimental Units
- 10. Some Remarks on the Spherical Distributions and Linear Models
- 11. On Computation of the Log-Likelihood Functions under Mixed Linear Models
- 12. Some Remarks on Improving Unbiased Estimators by Multiplication with a Constant
- 13. On Improving Estimation in a Restricted Gauss-Markov Model
- 14. Distribution of the Discriminant Function
- 15. Admissibility, Unbiasedness and Nonnegativity in the Balanced, Random, One-Way Anova Model
- 16. Inference in a General Linear Model with an Incorrect Dispersion Matrix
- 17. A Split-Plot Design with Wholeplot Treatments in an Incomplete Block Design
- 18. Sensitivity of Linear Models with Respect to the Covariance Matrix
- 19. On a Decomposition of the Singular Gauss-Markov Model
- 20. Ridge Type M-Estimators
- 21. Majorization and Approximate Majorization for Families of Measures, Applications to Local Comparison of Experiments and the Theory of Majorization of Vectors in Rn
- 22. Characterization of Linear Admissible Estimators in the Gauss-Markov Model under Normality