| 书目名称 | Linear Model Theory | | 副标题 | With Examples and Ex | | 编辑 | Dale L. Zimmerman | | 视频video | | | 概述 | Gives as much emphasis to predictive inference as it does to estimation, which is unique for a book on linear models.Illustrates every major theorem or concept with at least one special case or exampl | | 图书封面 |  | | 描述 | .This textbook presents a unified and rigorous approach to best linear unbiased estimation and prediction of parameters and random quantities in linear models, as well as other theory upon which much of the statistical methodology associated with linear models is based. The single most unique feature of the book is that each major concept or result is illustrated with one or more concrete examples or special cases. Commonly used methodologies based on the theory are presented in methodological interludes scattered throughout the book, along with a wealth of exercises that will benefit students and instructors alike. Generalized inverses are used throughout, so that the model matrix and various other matrices are not required to have full rank. Considerably more emphasis is given to estimability, partitioned analyses of variance, constrained least squares, effects of model misspecification, and most especially prediction than in many other textbooks on linear models. This book is intended for master and PhD students with a basic grasp of statistical theory, matrix algebra and applied regression analysis, and for instructors of linear models courses. Solutions to the book’s exercises | | 出版日期 | Textbook 2020 | | 关键词 | 62J05, 62J10, 62F03, 62F10, 62F25; linear models; statistical theory; regression methods; generalized in | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-52063-2 | | isbn_softcover | 978-3-030-52065-6 | | isbn_ebook | 978-3-030-52063-2 | | copyright | Springer Nature Switzerland AG 2020 |
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发表于 2025-3-21 19:50:06
