| 書目名稱 | Model Selection and Multimodel Inference | | 副標(biāo)題 | A Practical Informat | | 編輯 | Kenneth P. Burnham,David R. Anderson | | 視頻video | http://file.papertrans.cn/636/635799/635799.mp4 | | 概述 | Includes supplementary material: | | 圖書封面 |  | | 描述 | We wrote this book to introduce graduate students and research workers in various scienti?c disciplines to the use of information-theoretic approaches in the analysis of empirical data. These methods allow the data-based selection of a “best” model and a ranking and weighting of the remaining models in a pre-de?ned set. Traditional statistical inference can then be based on this selected best model. However, we now emphasize that information-theoretic approaches allow formal inference to be based on more than one model (m- timodel inference). Such procedures lead to more robust inferences in many cases, and we advocate these approaches throughout the book. The second edition was prepared with three goals in mind. First, we have tried to improve the presentation of the material. Boxes now highlight ess- tial expressions and points. Some reorganization has been done to improve the ?ow of concepts, and a new chapter has been added. Chapters 2 and 4 have been streamlined in view of the detailed theory provided in Chapter 7. S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in C | | 出版日期 | Book 2002Latest edition | | 關(guān)鍵詞 | Estimator; Inference; Likelihood; Model Selection; data analysis; information theory | | 版次 | 2 | | doi | https://doi.org/10.1007/b97636 | | isbn_softcover | 978-1-4419-2973-0 | | isbn_ebook | 978-0-387-22456-5 | | copyright | Springer Science+Business Media New York 2002 |
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