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

by T. Calinski (Editor)

Overview

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.

Product Details

ISBN-13:	9780792331360
Publisher:	Springer-Verlag New York, LLC
Publication date:	09/30/1994
Series:	Mathematics and Its Applications Series , #30
Pages:	324
Product dimensions:	6.14(w) x 9.21(h) x 0.75(d)

Table of Contents

Preface. Estimation, Prediction and Testing in Linear Models. Increments for (co)kriging with trend and pseudo-covariance estimation; L.C.A. Corsten. On the presentation of the minimax linear estimator in the convex linear model; H. Drygas, H. Läuter. Estimation of parameters in a special type of random effects model; J. Volaufová. Recent results in multiple testing: several treatments vs. a specified treatment; C.W. Dunnett. Multiple-multivariate-sequential T2-comparisons; C.P. Kitsos. On diagnosing collinearity-influential points in linear regression; H. Nyquist. Using nonnegative minimum biased quadratic estimation for variable selection in the linear regression model; S. Gnot, H. Knautz, G. Trenkler. Partial least squares and a linear model; D. von Rosen. Robustness. One-way analysis of variance under Tukey contamination: a small sample case simulation study; R. Zielinski. A note on robust estimation of parameters in mixed unbalanced models; T. Bednarski, S. Zontek. Optimal bias bounds for robust estimation in linear models; C.H. Müller. Estimation of Variance Components. Geometrical relations among variance component estimators; L.R. LaMotte. Asymptotic efficiencies of MINQUE and ANOVA variance component estimates in the nonnormal random model; P.H. Westfall. On asymptotic normality of admissible invariant quadratic estimators of variance components; S. Zontek. Admissible nonnegative invariant quadratic estimation in linear models with two variance components; S. Gnot, G. Trenkler, D. Stemann. 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; V. Guiard. Nonlinear Generalizations. Prediction domain in nonlinear models; S. Audrain, R. Tomassone. The geometry of nonlinear inference: accounting of prior and boundaries; A. Pázman. Design and Analysis of Experiments. General balance: artificial theory or practical relevance? R.A. Bailey. Optimality of generally balanced experimental block designs; B. Bogacka, S. Mejza. Optimality of the orthogonal block design for robust estimation under mixed models; R. Zmyślony, S. Zontek. On generalized binary proper efficiency-balanced block designs; A. Das, S. Kageyama. Design of experiments and neighbour methods; J.-M. Azaïs. A new look into composite designs; S. Ghosh, W.S. Al-Sabah. Using the complex linear model to search for an optimal juxtaposition of regular fractions; H. Monod, A. Kobilinsky. Some directions in comparison of linear experiments: a review; C. Stępniak, Z. Otachel. Properties of comparison criteria of normal experiments; J. Hauke, A. Markiewicz. Miscellanea. Characterizations of oblique and orthogonal projectors; G. Trenkler. Asymptotic properties of least squares parameter estimators in a dynamic errors-in-variables model; J. ten Vregelaar. A generic look at factor analysis; M. Lejeune. On Q-covariance and its applications; A. Krajka, D. Szynal. Contributor Index. Subject Index.