Applied Linear Regression: (Probability & Mathematical Statistics S.)

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 and 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.

Table of Contents:
Simple Linear Regression. Multiple Regression. Drawing Conclusions. Weighted Least Squares, Testing for Lack of Fit, General F-tests, and Confidence Ellipsoids. Diagnostics I: Residuals and Influence. Diagnostics II: Symptoms and Remedies. Model Building I: Defining New Predictors. Model Building II: Collinearity and Variable Selection. Prediction. Incomplete Data. Non-least Squares Estimation. Generalizations of Linear Regression. Appendixes. Tables. References. Symbol Index. Index.

About the Author :
Sanford Weisberg is associate professor and director of the Statistical Center at the University of Minnesota. The coauthor of Residuals and Influence in Regression (1982) and an associate editor of Journal of the American Statistical Association, Dr. Weisberg received his PhD in statistics from Harvard University.