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About this product
- Author(s)Tadeusz Calinski,W. Klonecki
- PublisherSpringer-Verlag New York Inc.
- Date of Publication22/01/1986
- Series TitleLecture Notes in Statistics
- Series Part/Volume Number35
- Place of PublicationNew York, NY
- Country of PublicationUnited States
- ImprintSpringer-Verlag New York Inc.
- Content Notebiography
- Weight567 g
- Width170 mm
- Height244 mm
- Spine17 mm
- Format DetailsTrade paperback (US)
- Table Of Contents1. 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.
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