Ebook: Linear Statistical Inference: Proceedings of the International Conference held at Poznań, 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 Sciences. The purpose of the meeting was to bring together scientists from vari­ ous countries working in the diverse areas of statistical sciences but showing great interest in the advances of research on linear inference taken in its broad sense. Thus, the conference programme included ses­ sions on Gauss-Markov models, robustness, variance components~ experi­ mental design, multiple comparisons, multivariate models, computational aspects and on some special topics. 38 papers were read within the vari­ ous sessions and 5 were presented as posters. At the end of the confer­ ence a lively general discussion session was held. The conference gathered more than ninety participants from 16 countries, representing both parts of Europe, North America and Asia. Judging from opinions expressed by many participants, the conference was quite suc­ cessful, well contributing to the dissemination of knowledge and the stimulation of research in different areas linked with statistical li­ near inference. If the conference was really a success, it was due to all its participants who in various ways were devoting their time and efforts to make the conference fruitful and enjoyable.

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Content:
Front Matter....Pages N2-VI
Some Geometric Tools for the Gaussian Linear Model with Applications to the Analysis of Residuals....Pages 1-19
Approximate Design Theory for a Simple Block Design with Random Block Effects....Pages 20-28
Rectangular Lattices Revisited....Pages 29-38
Multiple Comparisons between Several Treatments and a Specified Treatment....Pages 39-47
Minimax Prediction in Linear Models....Pages 48-60
Singular Information Matrices, Directional Derivatives, and Subgradients in Optimal Design Theory....Pages 61-77
A Note on Admissibility of Improved Unbiased Estimators in Two Variance Component Models....Pages 78-87
Linear Statistical Inference Based on L-Estimators....Pages 88-98
Connected Designs with the Minimum Number of Experimental Units....Pages 99-117
Some Remarks on the Spherical Distributions and Linear Models....Pages 118-134
On Computation of the Log-Likelihood Functions under Mixed Linear Models....Pages 135-149
Some Remarks on Improving Unbiased Estimators by Multiplication with a Constant....Pages 150-161
On Improving Estimation in a Restricted Gauss-Markov Model....Pages 162-169
Distribution of the Discriminant Function....Pages 170-183
Admissibility, Unbiasedness, and Nonnegativity in the Balanced, Random, One-Way Anova Model....Pages 184-199
On Inference in a General Linear Model with an Incorrect Dispersion Matrix....Pages 200-210
A Split-Plot Design with Wholeplot Treatments in an Incomplete Block Design....Pages 211-222
Sensitivity of Linear Models with Respect to the Covariance Matrix....Pages 223-230
On a Decomposition of the Singular Gauss-Markov Model....Pages 231-245
Ridge Type M-Estimators....Pages 246-258
Majorization and Approximate Majorization for Families of Measures, Applications to Local Comparison of Experiments and the Theory of Majorization of Vectors in Rn (Schur Convexity)....Pages 259-310
Characterization of Linear Admissible Estimators in Gauss-Markov Model under Normality....Pages 311-317
Back Matter....Pages 318-319

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Content:
Front Matter....Pages N2-VI
Some Geometric Tools for the Gaussian Linear Model with Applications to the Analysis of Residuals....Pages 1-19
Approximate Design Theory for a Simple Block Design with Random Block Effects....Pages 20-28
Rectangular Lattices Revisited....Pages 29-38
Multiple Comparisons between Several Treatments and a Specified Treatment....Pages 39-47
Minimax Prediction in Linear Models....Pages 48-60
Singular Information Matrices, Directional Derivatives, and Subgradients in Optimal Design Theory....Pages 61-77
A Note on Admissibility of Improved Unbiased Estimators in Two Variance Component Models....Pages 78-87
Linear Statistical Inference Based on L-Estimators....Pages 88-98
Connected Designs with the Minimum Number of Experimental Units....Pages 99-117
Some Remarks on the Spherical Distributions and Linear Models....Pages 118-134
On Computation of the Log-Likelihood Functions under Mixed Linear Models....Pages 135-149
Some Remarks on Improving Unbiased Estimators by Multiplication with a Constant....Pages 150-161
On Improving Estimation in a Restricted Gauss-Markov Model....Pages 162-169
Distribution of the Discriminant Function....Pages 170-183
Admissibility, Unbiasedness, and Nonnegativity in the Balanced, Random, One-Way Anova Model....Pages 184-199
On Inference in a General Linear Model with an Incorrect Dispersion Matrix....Pages 200-210
A Split-Plot Design with Wholeplot Treatments in an Incomplete Block Design....Pages 211-222
Sensitivity of Linear Models with Respect to the Covariance Matrix....Pages 223-230
On a Decomposition of the Singular Gauss-Markov Model....Pages 231-245
Ridge Type M-Estimators....Pages 246-258
Majorization and Approximate Majorization for Families of Measures, Applications to Local Comparison of Experiments and the Theory of Majorization of Vectors in Rn (Schur Convexity)....Pages 259-310
Characterization of Linear Admissible Estimators in Gauss-Markov Model under Normality....Pages 311-317
Back Matter....Pages 318-319
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