Robust Diagnostic Regression Analysis

1 Some Regression Examples.- 1.1 Influence and Outliers.- 1.2 Three Examples.- 1.3 Checking and Building Models.- 2 Regression and the Forward Search.- 2.1 Least Squares.- 2.2 Added Variables.- 2.3 Deletion Diagnostics.- 2.4 The Mean Shift Outlier Model.- 2.5 Simulation Envelopes.- 2.6 The Forward Search.- 2.7 Further Reading.- 2.8 Exercises.- 2.9 Solutions.- 3 Regression.- 3.1 Hawkins' Data.- 3.2 Stack Loss Data.- 3.3 Salinity Data.- 3.4 Ozone Data.- 3.5 Exercises.- 3.6 Solutions.- 4 Transformations to Normality.- 4.1 Background.- 4.2 Transformations in Regression.- 4.3 Wool Data.- 4.4 Poison Data.- 4.5 Modified Poison Data.- 4.6 Doubly Modified Poison Data: An Example of Masking.- 4.7 Multiply Modified Poison Data-More Masking.- 4.8 Ozone Data.- 4.9 Stack Loss Data.- 4.10 Mussels' Muscles: Transformation of the Response.- 4.11 Transforming Both Sides of a Model.- 4.12 Shortleaf Pine.- 4.13 Other Transformations and Further Reading.- 4.14 Exercises.- 4.15 Solutions.- 5 Nonlinear Least Squares.- 5.1 Background.- 5.2 The Forward Search.- 5.3 Radioactivity and Molar Concentration of Nifedipene.- 5.4 Enzyme Kinetics.- 5.5 Calcium Uptake.- 5.6 Nitrogen in Lakes.- 5.7 Isomerization ofn-Pentane.- 5.8 Related Literature.- 5.9 Exercises.- 5.10 Solutions.- 6 Generalized Linear Models.- 6.1 Background.- 6.2 The Exponential Family.- 6.3 Mean, Variance, and Likelihood.- 6.4 Maximum Likelihood Estimation.- 6.5 Inference.- 6.6 Checking Generalized Linear Models.- 6.7 Gamma Models.- 6.8 Car Insurance Data.- 6.9 Dielectric Breakdown Strength.- 6.10 Poisson Models.- 6.11 British Train Accidents.- 6.12 Cellular Differentiation Data.- 6.13 Binomial Models.- 6.14 Bliss's Beetle Data.- 6.15 Mice with Convulsions.- 6.16 Toxoplasmosis and Rainfall.- 6.17 Binary Data.- 6.18 Theory: TheEffect of Perfect Fit and the Arcsine Link.- 6.19 Vasoconstriction Data and Perfect Fit.- 6.20 Chapman Data.- 6.21 Developments and Further Reading.- 6.22 Exercises.- 6.23 Solutions.- A Data.- Author Index.

From the reviews:

MATHEMATICAL REVIEWS

"The text is lucidly written and theoretical discussions are amply complemented with examples and exercises. The book is accessible to students and users of statistical methods. It could, without doubt, serve as a textbook for courses on applied regression and generalized linear models. It will be a welcome addition to the resources of any applied statistician."

TECHNOMETRICS

"I would recommend practitioners of regression, this is, probably most of us, to read and use this book."

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

"I would recommend ROBUST DIAGNOSTICS REGRESSION ANALYSIS and tools for anyone who does a fair amount of applied regression analysis on small- to moderate-sized datasets. It would be especially useful for anyone who uses nonlinear regression and/or generalized linear regression, where many fewer diagnostic tools are available. As a textbook, it would be good as a supplemental or even a primary text in a masters-level regression course. Researchers in other fields who do their own regression analysis also should be referred to this text, which they will find quite understandable."

"This book presents a host of graphical tools for regression data analysis, based on the 'forward search' approach. ... In order to make the work accessible to users, the authors have developed a number of SPlus programs. ... it would be an excellent reference book for students as well as other data analysts." (Debasis Sengupta, Sankhya, Vol. 65 (4), 2003)

"The topic of this monograph is the use of certain types of regression diagnostics, based on a robust forward search ... . the monograph is absolutely worth reading. ... The monograph is set up in a very instructive manner ... . The material is treated in a clear way, the book is very well written and understandable. Summarizing, this monograph can be recommended to readers who want to learn ...