9783540742265 - linear models and generalizations: least squares and alternatives (springer series in statistics) von rao, c. radhakrishna; toutenburg, helge; shalabh; heumann, christian (2 Ergebnisse)
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Sprache: Englisch
Verlag: Springer, 2007
- Hardcover
Anbieter: Buchkanzlei, Bremen, DeutschlandBuchkanzlei
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Gebraucht - Sehr gut
EUR 55,40
EUR 33,00 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Hardcover. Zustand: Sehr gut. Third extended Edition. 591 pp. Very well preserved copy with only slight signs of wear 324 Sprache: Englisch Gewicht in Gramm: 1213.
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Sprache: Englisch
Verlag: Springer, 2007
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 134,63
EUR 65,26 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Buch. 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 a…nd 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.
