| 書目名稱 | Numerical Analysis for Statisticians | | 編輯 | Kenneth Lange | | 視頻video | http://file.papertrans.cn/669/668938/668938.mp4 | | 叢書名稱 | Statistics and Computing | | 圖書封面 |  | | 描述 | This book, like many books, was born in frustration. When in the fall of 1994 I set out to teach a second course in computational statistics to d- toral students at the University of Michigan, none of the existing texts seemed exactly right. On the one hand, the many decent, even inspiring, books on elementary computational statistics stress the nuts and bolts of using packaged programs and emphasize model interpretation more than numerical analysis. On the other hand, the many theoretical texts in - merical analysis almost entirely neglect the issues of most importance to statisticians. TheclosestbooktomyidealwastheclassicaltextofKennedy and Gentle [2]. More than a decade and a half after its publication, this book still has many valuable lessons to teach statisticians. However, upon re?ecting on the rapid evolution of computational statistics, I decided that the time was ripe for an update. The book you see before you represents a biased selection of those topics in theoretical numerical analysis most relevant to statistics. By intent this book is not a compendium of tried and trusted algorithms, is not a c- sumer’s guide to existing statistical software, and is not an exposition | | 出版日期 | Textbook 19991st edition | | 關(guān)鍵詞 | Markov chain Monte Carlo; Newton‘s method; STATISTICA; algorithms; expectation–maximization algorithm; li | | 版次 | 1 | | doi | https://doi.org/10.1007/b98850 | | isbn_ebook | 978-0-387-22724-5Series ISSN 1431-8784 Series E-ISSN 2197-1706 | | issn_series | 1431-8784 | | copyright | Springer Science+Business Media New York 1999 |
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