Linear Models and Generalizations : Least Squares and Alternatives / by C. Radhakrishna Rao, Shalabh, Helge Toutenburg, Christian Heumann

Mitwirkende(r):

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Springer Series in Statistics | SpringerLink BücherVerlag: Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2008Auflage: Third Extended EditionBeschreibung: Online-Ressource (digital)ISBN:

9783540742272

Schlagwörter:

Andere physische Formen: 9783540742265 | Buchausg. u.d.T.: Linear models and generalizations. 3., extended ed. New York : Springer, 2008. XIX, 570 S.DDC-Klassifikation:

519.5
519.2 23
519.5/36
510

MSC: MSC: *62J05 | 62J12 | 62-02 | 62-01 | 62F35 | 62F30RVK: RVK: SK 840 | QH 234LOC-Klassifikation:

QA276-280
QA279

DOI: DOI: 10.1007/978-3-540-74227-2Online-Ressourcen:

Zugang im Netz des KIT

Zusammenfassung: The Simple Linear Regression Model -- The Multiple Linear Regression Model and Its Extensions -- The Generalized Linear Regression Model -- Exact and Stochastic Linear Restrictions -- Prediction in the Generalized Regression Model -- Sensitivity Analysis -- Analysis of Incomplete Data Sets -- Robust Regression -- Models for Categorical Response Variables.Zusammenfassung: Thebookisbasedonseveralyearsofexperienceofbothauthorsinteaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and o?ers a selectionofclassicalandmodernalgebraicresultsthatareusefulinresearch work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results aboutthe de?niteness ofmatrices,especially forthe di?erences ofmatrices, which enable superiority comparisons of two biased estimates to be made for the ?rst time. We have attempted to provide a uni?ed theory of inference from linear models with minimal assumptions. Besides the usual least-squares theory, alternative methods of estimation and testing based on convex loss fu- tions and general estimating equations are discussed. Special emphasis is given to sensitivity analysis and model selection. A special chapter is devoted to the analysis of categorical data based on logit, loglinear, and logistic regression models. The material covered, theoretical discussion, and a variety of practical applications will be useful not only to students but also to researchers and consultants in statistics.PPN: PPN: 1645976610Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SXMS | ZDB-2-SMA

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