LINSTAT '93 : Proceedings of the International Conference on Linear Statistical Inference

By: Tadeusz Calinski (Editor), Radoslaw Kala (Editor)

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328 Pages

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This volume contains a selection of invited and contributed papers presented at the International Conference on Linear Statistical Inference LINSTAT '93, held in Poznan, Poland, from May 31 to June 4, 1993. Topics treated include estimation, prediction and testing in linear models, robustness of relevant statistical methods, estimation of variance components appearing in linear models, generalizations to nonlinear models, design and analysis of experiments, including optimality and comparison of linear experiments. This book will be of interest to mathematical statisticians, applied statisticians, biometricians, biostatisticians, and econometrists.

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Non-Fiction Mathematics Probability & Statistics Geometry Algebraic Geometry Algebra Applied Mathematics

Preface
Scientific Programme of the Conference
Increments for (co)kriging with trend and pseudo-covariance estimation	p. 1
On the presentation of the minimax linear estimator in the convex linear model	p. 13
Estimation of parameters in a special type of random effects model	p. 27
Recent results in multiple testing: several treatments vs. a specified treatment	p. 35
Multiple-multivariate-sequential T[superscript 2]-comparisons	p. 47
On diagnosing collinearity-influential points in linear regression	p. 53
Using nonnegative minimum biased quadratic estimation for variable selection in the linear regression model	p. 65
Partial least squares and a linear model	p. 73
One-way analysis of variance under Tukey contamination: a small sample case simulation study	p. 79
A note on robust estimation of parameters in mixed unbalanced models	p. 87
Optimal bias bounds for robust estimation in linear models	p. 97
Geometrical relations among variance component estimators	p. 103
Asymptotic efficiencies of MINQUE and ANOVA variance component estimates in the nonnormal random model	p. 113
On asymptotic normality of admissible invariant quadratic estimators of variance components	p. 121
Admissible nonnegative invariant quadratic estimation in linear models with two variance components	p. 129
About the multimodality of the likelihood function when estimating the variance components in a one-way classification by means of the ML or REML method	p. 139
Prediction domain in nonlinear models	p. 147
The geometry of nonlinear inference: accounting of prior and boundaries	p. 159
General balance: artificial theory or practical relevance?	p. 171
Optimality of generally balanced experimental block designs	p. 185
Optimality of the orthogonal block design for robust estimation under mixed models	p. 195
On generalized binary proper efficiency-balanced block designs	p. 203
Design of experiments and neighbour methods	p. 211
A new look into composite designs	p. 223
Using the complex linear model to search for an optimal juxtaposition of regular fractions	p. 229
Some directions in comparison of linear experiments: a review	p. 239
Properties of comparison criteria of normal experiments	p. 245
Characterizations of oblique and orthogonal projectors	p. 255
Asymptotic properties of least squares parameter estimators in a dynamic errors-in-variables model	p. 271
A generic look at factor analysis	p. 285
On Q-covariance and its applications	p. 293
List of Referees	p. 301
Contributor Index	p. 303
Subject Index	p. 305
Table of Contents provided by Blackwell. All Rights Reserved.

LINSTAT '93 : Proceedings of the International Conference on Linear Statistical Inference

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