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Plane Answers to Complex Questions: The Theory of Linear Models: Springer Texts in Statistics

Autor Ronald Christensen
en Limba Engleză Paperback – 3 dec 2010
The third edition of Plane Answers includes fundamental changes in how some aspects of the theory are handled. Chapter 1 includes a new section that introduces generalized linear models. Primarily, this provides a defini­ tion so as to allow comments on how aspects of linear model theory extend to generalized linear models. For years I have been unhappy with the concept of estimability. Just because you cannot get a linear unbiased estimate of something does not mean you cannot estimate it. For example, it is obvious how to estimate the ratio of two contrasts in an ANOVA, just estimate each one and take their ratio. The real issue is that if the model matrix X is not of full rank, the parameters are not identifiable. Section 2.1 now introduces the concept of identifiability and treats estimability as a special case of identifiability. This change also resulted in some minor changes in Section 2.2. In the second edition, Appendix F presented an alternative approach to dealing with linear parametric constraints. In this edition I have used the new approach in Section 3.3. I think that both the new approach and the old approach have virtues, so I have left a fair amount of the old approach intact. Chapter 8 contains a new section with a theoretical discussion of models for factorial treatment structures and the introduction of special models for homologous factors. This is closely related to the changes in Section 3.3.
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Specificații

ISBN-13: 9781441929716
ISBN-10: 1441929711
Pagini: 500
Ilustrații: XIX, 476 p. 1 illus.
Dimensiuni: 155 x 235 x 26 mm
Greutate: 0.69 kg
Ediția:Softcover reprint of hardcover 3rd ed. 2002
Editura: Springer
Colecția Springer
Seria Springer Texts in Statistics

Locul publicării:New York, NY, United States

Public țintă

Research

Cuprins

1 Introduction.- 2 Estimation.- 3 Testing Hypotheses.- 4 One-Way ANOVA.- 5 Multiple Comparison Techniques.- 6 Regression Analysis.- 7 Multifactor Analysis of Variance.- 8 Experimental Design Models.- 9 Analysis of Covariance.- 10 Estimation and Testing in General Gauss-Markov Models.- 11 Split Plot Models.- 12 Mixed Models and Variance Components.- 13 Checking Assumptions, Residuals, and Influential Observations.- 14 Variable Selection and Collinearity.- Appendix A: Vector Spaces.- Appendix B: Matrix Results.- B.1 Basic Ideas.- B.2 Eigenvalues and Related Results.- B.3 Projections.- B.4 Miscellaneous Results.- B.5 Properties of Kronecker Products and Vec Operators.- B.6 Tensors.- B.7 Exercises.- Appendix C: Some Univariate Distributions.- Appendix D: Multivariate Distributions.- Appendix E: Inference for One Parameter.- E.1 Confidence Intervals.- E.2 Hypothesis Testing.- Appendix F: Significantly Insignificant Tests.- F.1 Lack of Fit and Small F Statistics.- F.2 The Effect of Correlation and Heteroscedasticity on F Statistics.- Appendix G: Randomization Theory Models.- G.1 Simple Random Sampling.- G.2 Completely Randomized Designs.- G.3 Randomized Complete Block Designs.- References.- Author Index.

Recenzii

From the reviews of the third edition:
"This well-written and interesting book can serve as a textbook for a graduate-level course in linear model theory and its applications, and as a reference book for a wide range of definitions and results associated with particular linear models." Journal of the American Statistical Assoc.
"The following quotations are taken from the (same) reviewer's comments on the second edition (Short Book Reviews, Vol.17/1, April 1997, p.4): The book "retains its fairly mathematical character... The writing style is inviting... friendly and affable... The computing aspects of regression are de-emphasized and the text leans more towards well-prepared students." All are still true, and I once again recommend the book for the indicated target audience." ISI Short Book Reviews, Vol. 22/3, 2002
"This book with the unusual title has been quite popular because of its lucid treatment. What I like most about the book is that many important observations have been made in an entertaining manner. … In this edition the idea of identifiability has rightly been given more emphasis than estimability, which sets this book apart from most other books on linear models. … I have always regarded this book as a must-read for serious users of linear models. The third edition makes it even better.” (Debasis Sengupta, Sankhya, Vol. 65 (4), 2003)
"This is the third edition of a popular textbook in general linear models aimed at graduate students. … The appealing features of this book lie in its projection-based dogma and its thought-provoking conversational prose. It continues to serve as an authoritative, well-written, polished linear models text that is useful both as a reference and as a graduate course text." (Robert Lund, Journal of the American Statistical Association, Vol. 98 (463), September, 2003)
"The book ‘retains its fairly mathematical character. The writing style is inviting,friendly and affable. The computing aspects of regression are de-emphasized and the text leans more towards well-prepared students.’ All are still true, and I once again recommend the book for the indicated target audience." (N. R. Draper, Short Book Reviews, Vol. 22 (3), 2002)
"This 3rd edition, like the preceding editions, illustrates the practical applications of projective approach to linear models. … elegant treatment of identifiability and estimability and their connection. … Excellent examples are used to illustrate the effect of high leverage. … The intended audience for this book appears to be first-year graduate students. … I feel that Plane Answers to Complex Questions is a nice addition to the literature on linear models. … excellent references for practitioners." (Felix Famoye, Technometrics, Vol. 45 (2), May, 2003)

Notă biografică

Ronald Christensen is a Professor of Statistics at the University of New Mexico, Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics, former Chair of the ASA Section on Bayesian Statistical Science and former Editor of The American Statistician. His book publications include Advanced Linear Modeling (Springer, new edition forthcoming), Log-Linear Models and Logistic Regression (Springer 1997), Analysis of Variance, Design, and Regression (1996, 2016), and  Bayesian Ideas and Data Analysis (2010, with Johnson, Branscum and Hanson).

Textul de pe ultima copertă

This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples that make it ideal for a graduate-level course. All of the standard topics are covered in depth: estimation including biased and Bayesian estimation, significance testing, ANOVA, multiple comparisons, regression analysis, and experimental design models.  In addition, the book covers topics that are not usually treated at this level, but which are important in their own right: best linear and best linear unbiased prediction, split plot models, balanced incomplete block designs, testing for lack of fit, testing for independence, models with singular covariance matrices, diagnostics, collinearity, and variable selection. This new edition includes new sections on alternatives to least squares estimation and the variance-bias tradeoff, expanded discussion of variable selection, new material on characterizing the interaction space in an unbalanced two-way ANOVA, Freedman's critique of the sandwich estimator, and much more.

Caracteristici

Features exercises throughout, with additional exercises supplied at the end of each chapter so that readers can retain theory Illustrates the practical application of the projective approach to linear models Includes appendices that with prerequisite background information on linear algebra and mathematical statistics Prepared in conjunction with a new edition of Christensen's Advanced Linear Modeling, so that advanced undergraduate and graduate students have access to a wealth of revised content in statistical theory Provides access to accompanying computer code Includes supplementary material: sn.pub/extras