James H. Stapleton

Linear Statistical Models 2e

• Gebundenes Buch

Produktbeschreibung

Praise for the First Edition

"This impressive and eminently readable text . . . [is] a welcome addition to the statistical literature."
--The Indian Journal of Statistics

Revised to reflect the current developments on the topic, Linear Statistical Models, Second Edition provides an up-to-date approach to various statistical model concepts. The book includes clear discussions that illustrate key concepts in an accessible and interesting format while incorporating the most modern software applications.

This Second Edition follows an introduction-theorem-proof-examples format that allows for easier comprehension of how to use the methods and recognize the associated assumptions and limits. In addition to discussions on the methods of random vectors, multiple regression techniques, simultaneous confidence intervals, and analysis of frequency data, new topics such as mixed models and curve fitting of models have been added to thoroughly update and modernize the book. Additional topical coverage includes:
An introduction to R and S-Plus(r) with many examples
Multiple comparison procedures
Estimation of quantiles for regression models
An emphasis on vector spaces and the corresponding geometry

Extensive graphical displays accompany the book's updated descriptions and examples, which can be simulated using R, S-Plus(r), and SAS(r) code. Problems at the end of each chapter allow readers to test their understanding of the presented concepts, and additional data sets are available via the book's FTP site.

Linear Statistical Models, Second Edition is an excellent book for courses on linear models at the upper-undergraduate and graduate levels. It also serves as a comprehensive reference for statisticians, engineers, and scientists who apply multiple regression or analysis of variance in their everyday work.

Produktdetails

• Wiley Series in Probability and Statistics
• Verlag: Wiley & Sons
• 2. Aufl.
• Seitenzahl: 512
• Erscheinungstermin: 1. August 2009
• Englisch
• Abmessung: 240mm x 161mm x 32mm
• Gewicht: 805g
• ISBN-13: 9780470231463
• ISBN-10: 0470231467
• Artikelnr.: 26431388

Autorenporträt

James H. Stapleton, PhD, is Professor Emeritus in the Department of Statistics and Probability at Michigan State University. He is the author of Models for Probability and Statistical Inference: Theory and Applications, also published by Wiley.

Inhaltsangabe

Preface. 1 Linear Algebra, Projections. 1.1 Introduction. 1.2 Vectors,
Inner Products, Lengths. 1.3 Subspaces, Projections. 1.4 Examples. 1.5 Some
History. 1.6 Projection Operators. 1.7 Eigenvalues and Eigenvectors. 2
Random Vectors. 2.1 Covariance Matrices. 2.2 Expected Values of Quadratic
Forms. 2.3 Projections of Random Variables. 2.4 The Multivariate Normal
Distribution. 2.5 The Chi2, F, and t Distributions. 3 The Linear Model. 3.1
The Linear Hypothesis. 3.2 Confidence Intervals and Tests on * = c1ß1 + . .
. + ckßk. 3.3 The Gauss-Markov Theorem. 3.4 The Gauss-Markov Theorem For
The General Case. 3.5 Interpretation of Regression Coefficients. 3.6 The
Multiple Correlation Coefficient. 3.7 The Partial Correlation Coefficient.
3.8 Testing H0 : theta epsilon V0 C V. 3.9 Further Decomposition of
Subspaces. 3.10 Power of the F-Test. 3.11 Confidence and Prediction
Intervals. 3.12 An Example from SAS. 3.13 Another Example: Salary Data. 4
Fitting of Regression Models. 4.1 Linearizing Transformations. 4.2
Specification Error. 4.3 Generalized Least Squares. 4.4 Effects of
Additional or Fewer Observations. 4.5 Finding the "Best" Set of Regressors.
4.6 Examination of Residuals. 4.7 Collinearity. 4.8 Asymptotic Normality.
4.9 Spline Functions. 4.10 Nonlinear Least Squares. 4.11 Robust Regression.
4.12 Bootstrapping in Regression. 4.13 Quantile Regression. 5 Simultaneous
Confidence Intervals. 5.1 Bonferroni Confidence Intervals. 5.2 Scheffé
Simultaneous Confidence Intervals. 5.3 Tukey Simultaneous Confidence
Intervals. 5.4 Comparison of Lengths. 5.5 Bechhofer's Method. 6 Two-and
Three-Way Analyses of Variance. 6.1 Two-Way Analysis of Variance. 6.2
Unequal Numbers of Observations Per Cell. 6.3 Two-Way Analysis of Variance,
One Observation Per Cell. 6.4 Design of Experiments. 6.5 Three-Way Analysis
of Variance. 6.6 The Analysis of Covariance. 7 Miscellaneous Other Models.
7.1 The Random Effects Model. 7.2 Nesting. 7.3 Split Plot Designs. 7.4
Mixed Models. 7.5 Balanced Incomplete Block Designs. 8 Analysis of
Frequency Data. 8.1 Examples. 8.2 Distribution Theory. 8.3 Conf. Ints. on
Poisson and Binomial Parameters. 8.4 Log-Linear Models. 8.5 Estimation for
the Log-Linear Model. 8.6 Chi-Square Goodness-of-Fit Statistics. 8.7
Limiting Distributions of the Estimators. 8.8 Logistic Regression. The
Statistical Language R. Answers. Index.