Linear Statistical Models
2nd Edition
Published on 4. September 2009
Book
Hardback
474 pages
978-0-470-23146-3 (ISBN)
Description
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.
More details
Product info
gebunden
Series
Edition
2. Auflage
Language
English
Place of publication
New York
United States
Target group
College/higher education
Product notice
sewn/stitched
Cloth over boards
Illustrations
Charts: 10 B&W, 0 Color; Drawings: 5 B&W, 0 Color; Graphs: 100 B&W, 0 Color
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 32 mm
Weight
921 gr
ISBN-13
978-0-470-23146-3 (9780470231463)
Schweitzer Classification
Other editions
Previous edition
James H. Stapleton
Linear Statistical Models
Book
07/1995
Wiley
€123.80
Article exhausted; check for reprint
Person
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.
Content
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.