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    UID:
    almafu_BV019315745
    Format: XXI, 621 S. : , graph. Darst.
    ISBN: 0-387-40852-5 , 978-0-387-40852-1
    Series Statement: Springer series in statistics
    Note: Literaturverz. S. 597 - 606
    Language: English
    Subjects: Economics , Mathematics
    RVK:
    RVK:
    Keywords: Multivariate Analyse
    Author information: Atkinson, Anthony C. 1937-
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Online Resource
    Online Resource
    New York, NY :Springer New York :
    UID:
    almahu_9947362853902882
    Format: XVI, 328 p. , online resource.
    ISBN: 9781461211600
    Series Statement: Springer Series in Statistics,
    Content: 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.
    Note: 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: The Effect 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.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9781461270270
    Language: English
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
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  • 4
    UID:
    almahu_9947362720502882
    Format: XXIV, 624 p. , online resource.
    ISBN: 9780387218403
    Series Statement: Springer Series in Statistics,
    Content: The forward search provides a method of revealing the structure of data through a mixture of model fitting and informative plots. The continuous multivariate data that are the subject of this book are often analyzed as if they come from one or more normal distributions. Such analyses, including the need for transformation, may be distorted by the presence of unidentified subsets and outliers, both individual and clustered. These important features are disguised by the standard procedures of multivariate analysis. The book introduces methods that reveal the effect of each observation on fitted models and inferences. The powerful methods of data analysis will be of importance to scientists and statisticians. Although the emphasis is on the analysis of data, theoretical developments make the book suitable for a graduate statistical course on multivariate analysis. Topics covered include principal components analysis, discriminant analysis, cluster analysis and the analysis of spatial data. S-Plus programs for the forward search are available on a web site. This book is a companion to Atkinson and Riani's Robust Diagnostic Regression Analysis of which the reviewer for The Journal of the Royal Statistical Society wrote "I read this book, compulsive reading such as it was, in three sittings." Anthony Atkinson is Emeritus Professor of Statistics at the London School of Economics. He is also the author of Plots, Transformations, and Regression and coauthor of Optimum Experimental Designs. Professor Atkinson has served as Editor of The Journal of the Royal Statistical Society, Series B.
    Note: 1 Examples of Multivariate Data -- 2 Multivariate Data and the Forward Search -- 3 Data from One Multivariate Distribution -- 4 Multivariate Transformations to Normality -- 5 Principal Components Analysis -- 6 Discriminant Analysis -- 7 Cluster Analysis -- 8 Spatial Linear Models -- Appendix: Tables of Data -- Author Index.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9781441923530
    Language: English
    URL: Volltext  (lizenzpflichtig)
    Library Location Call Number Volume/Issue/Year Availability
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