| 书目名称 | Linear Models and Generalizations |
| 副标题 | Least Squares and Al |
| 编辑 | C. Radhakrishna Rao,Shalabh,Christian Heumann |
| 视频video | |
| 概述 | Essential text for graduate statistics courses and courses where linear models play a part.Presents advanced research results and gives an overview of generalizations.New edition has been extensivley |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | 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 anal |
| 出版日期 | Textbook 2008Latest edition |
| 关键词 | Fitting; Generalized linear model; Least Squares; Likelihood; Optimization Theory; Regression; best fit; ca |
| 版次 | 3 |
| doi | https://doi.org/10.1007/978-3-540-74227-2 |
| isbn_softcover | 978-3-642-09353-1 |
| isbn_ebook | 978-3-540-74227-2Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer-Verlag Berlin Heidelberg 2008 |
发表于 2025-3-21 17:42:20
