Book Description
Nonlinear Statistical Methods A. Ronald Gallant Describes the recent advances in statistical and probability theory that have removed obstacles to an adequate theory of estimation and inference for nonlinear models. Thoroughly explains theory, methods, computations, and applications. Covers the three major categories of statistical models that relate dependent variables to explanatory variables: univariate regression models, multivariate regression models, and simultaneous equations models. Includes many figures which illustrate computations with SAS(R) code and resulting output. 1987 (0 471-80260-3) 610 pp. Exploring Data Tables, Trends, and Shapes Edited by David C. Hoaglin, Frederick Mosteller, and John W. Tukey Together with its companion volume, Understanding Robust and Exploratory Data Analysis, this work provides a definitive account of exploratory and robust/resistant statistics. It presents a variety of more advanced techniques and extensions of basic exploratory tools, explains why these further developments are valuable, and provides insight into how and why they were invented. In addition to illustrating these techniques, the book traces aspects of their development from classical statistical theory. 1985 (0 471-09776-4) 672 pp. Robust Regression & Outlier Detection Peter J. Rousseeuw and Annick M. Leroy An introduction to robust statistical techniques that have been developed to isolate or identify outliers. Emphasizes simple, intuitive ideas and their application in actual use. No prior knowledge of the field is required. Discusses robustness in regression, simple regression, robust multiple regression, the special case of one-dimensional location, and outlier diagnostics. Also presents an outlook of robustness in related fields such as time series analysis. Emphasizes "high-breakdown" methods that can cope with a sizable fraction of contamination. Focuses on the least median of squares method, which appeals to the intuition and is easy to use. 1987 (0 471-85233-3) 329 pp.
--Ce texte fait référence à une édition épuisée ou non disponible de ce titre.
Back Cover copy
Master linear regression techniques with a new edition of a classic text
Reviews of the Second Edition:
"I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression."
Technometrics, February 1987
"Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."
American Scientist, MayJune 1987
Applied Linear Regression, Third Edition has been thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, the Third Edition stresses the use of graphical methods in an effort to find appropriate models and to better understand them. In that spirit, most analyses and homework problems use graphs for the discovery of structure as well as for the summarization of results.
The Third Edition incorporates new material reflecting the latest advances, including:
*Use of smoothers to summarize a scatterplot
*Box-Cox and graphical methods for selecting transformations
*Use of the delta method for inference about complex combinations of parameters
*Computationally intensive methods and simulation, including the bootstrap method
*Expanded chapters on nonlinear and logistic regression
*Completely revised chapters on multiple regression, diagnostics, and generalizations of regression
Readers will also find helpful pedagogical tools and learning aids, including:
*More than 100 exercises, most based on interesting real-world data
*Web primers demonstrating how to use standard statistical packages, including R, S-Plus(r), SPSS(r), SAS(r), and JMP(r), to work all the examples and exercises in the text
*A free online library for R and S-Plus that makes the methods discussed in the book easy to use
With its focus on graphical methods and analysis, coupled with many practical examples and exercises, this is an excellent textbook for upper-level undergraduates and graduate students, who will quickly learn how to use linear regression analysis techniques to solve and gain insight into real-life problems.
Reviews of the Second Edition:
"I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression."
Technometrics, February 1987
"Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."
American Scientist, MayJune 1987
Applied Linear Regression, Third Edition has been thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, the Third Edition stresses the use of graphical methods in an effort to find appropriate models and to better understand them. In that spirit, most analyses and homework problems use graphs for the discovery of structure as well as for the summarization of results.
The Third Edition incorporates new material reflecting the latest advances, including:
*Use of smoothers to summarize a scatterplot
*Box-Cox and graphical methods for selecting transformations
*Use of the delta method for inference about complex combinations of parameters
*Computationally intensive methods and simulation, including the bootstrap method
*Expanded chapters on nonlinear and logistic regression
*Completely revised chapters on multiple regression, diagnostics, and generalizations of regression
Readers will also find helpful pedagogical tools and learning aids, including:
*More than 100 exercises, most based on interesting real-world data
*Web primers demonstrating how to use standard statistical packages, including R, S-Plus(r), SPSS(r), SAS(r), and JMP(r), to work all the examples and exercises in the text
*A free online library for R and S-Plus that makes the methods discussed in the book easy to use
With its focus on graphical methods and analysis, coupled with many practical examples and exercises, this is an excellent textbook for upper-level undergraduates and graduate students, who will quickly learn how to use linear regression analysis techniques to solve and gain insight into real-life problems.
