9783642093531 - Linear Models and Generalizations: Least Squares and Alternatives (Springer Series in Statistics) von Rao, C. Radhakrishna; Toutenburg, Helge; Shalabh; Heumann, Chr... (4 Ergebnisse)

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Taschenbuch. Zustand: Neu. Linear Models and Generalizations | Least Squares and Alternatives | C. Radhakrishna Rao (u. a.) | Taschenbuch | Springer Series in Statistics | xix | Englisch | 2010 | Springer | EAN 9783642093531 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Paperback. Zustand: Brand New. 3rd, extended ed. edition. 570 pages. 9.00x6.00x1.34 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.