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2000 | Buch

Robust Diagnostic Regression Analysis

verfasst von: Anthony Atkinson, Marco Riani

Verlag: Springer New York

Buchreihe : Springer Series in Statistics

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SUCHEN

Über dieses Buch

This book is about using graphs to understand the relationship between a regression model and the data to which it is fitted. Because of the way in which models are fitted, for example, by least squares, we can lose infor­ mation about the effect of individual observations on inferences about the form and parameters of the model. The methods developed in this book reveal how the fitted regression model depends on individual observations and on groups of observations. Robust procedures can sometimes reveal this structure, but downweight or discard some observations. The novelty in our book is to combine robustness and a forward" " search through the data with regression diagnostics and computer graphics. We provide easily understood plots that use information from the whole sample to display the effect of each observation on a wide variety of aspects of the fitted model. This bald statement of the contents of our book masks the excitement we feel about the methods we have developed based on the forward search. We are continuously amazed, each time we analyze a new set of data, by the amount of information the plots generate and the insights they provide. We believe our book uses comparatively elementary methods to move regression in a completely new and useful direction. We have written the book to be accessible to students and users of statistical methods, as well as for professional statisticians.

Inhaltsverzeichnis

Frontmatter
1. Some Regression Examples
Abstract
Regression analysis is the most widely used technique for fitting models to data. This book is not confined to regression, but we use three examples of regression to introduce our general ideas.
Anthony Atkinson, Marco Riani
2. Regression and the Forward Search
Abstract
The basic algebra of least squares is presented in the first section of the chapter, followed by that for added variables, which is used in the construction of some score tests for regression models, particularly that for transformations in Chapter 4. Related results are needed for testing the goodness of the link in a generalized linear model, Chapter 6. Several of the quantities monitored during the forward search come from considering the effect of deletion of an observation. Deletion diagnostics are described in §2.3 of the chapter and, in §2.4, related to the mean shift outlier model. Simulation envelopes are described in §2.5 and the forward search is defined and discussed in §2.6. The chapter concludes with some suggestions for further reading.
Anthony Atkinson, Marco Riani
3. Regression
Abstract
In this chapter we exemplify some of the theory of Chapter 2 for four sets of data. We start with some synthetic data that were designed to contain masked outliers and so provide difficulties for least squares diagnostics based on backwards deletion. We show that the data do indeed present such problems, but that our procedure finds the hidden structure.
Anthony Atkinson, Marco Riani
4. Transformations to Normality
Abstract
Several analyses in this book have been improved by using a transformation of the response, rather than the original response itself, in the analysis of the data. For the introductory example of the wool data in Chapter 1, the normal plot of residuals in Figure 1.9 is improved by working with log y rather than y ( Figure 4.2). The transformation improves the approximate normality of the errors. The transformation also improves the homogeneity of the errors. The plot of residuals against fitted values for the original data, also given in Figure 1.9, showed the variance of the residuals increasing with fitted value. The same plot for log y, given in Figure 4.2, shows no such increase.
Anthony Atkinson, Marco Riani
5. Nonlinear Least Squares
Abstract
In this chapter we extend our methods based on the forward search to regression models that are nonlinear in the parameters. Estimation is still by least squares although now iterative methods have to be used to find the parameter values minimizing the residual sum of squares. Even with normally distributed errors, the parameter estimates are not exactly normally distributed and contours of the sum of squares surfaces are not exactly ellipsoidal. The consequent inferential problems are usually solved by linearization of the model by Taylor series expansion, in effect ignoring the nonlinear aspects of the problem. The next section gives an outline of this material, booklength treatments of which are given by Bates and Watts (1988) and by Seber and Wild (1989). Both books describe the use of curvature measures to assess the effect of nonlinearity on approximate inferences using the linearized model. Since we find it informative to monitor measures of curvature during the forward search, we present a summary of the theory in §5.1.2. Ratkowsky (1983) uses measures of curvature to find parameter transformations that reduce curvature and so improve the performance of nonlinear least squares fitting routines.
Anthony Atkinson, Marco Riani
6. Generalized Linear Models
Abstract
In all examples in previous chapters it was assumed that the errors of observation were either normally distributed or, in Chapter 4, could be made approximately so by transformation. This chapter extends the class of models for the forward search to include generalized linear models. We give examples in which the errors of observation have the gamma distribution. For this continuous distribution the results are similar to those for the normal distribution. We also give examples of discrete data from the Poisson distribution and from the binomial. Interest again is in the relationship between the distribution of the response and the values of one or more explanatory variables. The distribution which is most unlike the normal is that for binary data, that is, binomial observations with one trial at each combination of factors.
Anthony Atkinson, Marco Riani
Backmatter
Metadaten
Titel
Robust Diagnostic Regression Analysis
verfasst von
Anthony Atkinson
Marco Riani
Copyright-Jahr
2000
Verlag
Springer New York
Electronic ISBN
978-1-4612-1160-0
Print ISBN
978-1-4612-7027-0
DOI
https://doi.org/10.1007/978-1-4612-1160-0