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| Artikel-Nr.: 5667A-9783540742265 Herst.-Nr.: 9783540742265 EAN/GTIN: 9783540742265 |
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![](/p.gif) | 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. Weitere Informationen: ![](/p.gif) | ![](/p.gif) | Author: | C. Radhakrishna Rao; M. Schomaker; Helge Toutenburg; Shalabh; Christian Heumann | Verlag: | Springer Berlin | Sprache: | eng |
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![](/p.gif) | Weitere Suchbegriffe: Regression (mathematisch), Fitting; Generalized linear model; Likelihood; Optimization theory; Regression; best fit; calculus; Econometrics; linear regression; Optimization; Statistics, Fitting, Generalized linear model, Least Squares, Likelihood, Optimization Theory, Regression, best fit, calculus, econometrics |
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