| 书目名称 | Numerical Analysis for Statisticians | | 编辑 | Kenneth Lange | | 视频video | | | 概述 | Serves as a graduate text for a survey of computational statistics.Second edition adds material on optimization, MM algorithm, penalty and barrier methods, and model selection via the lasso.Other majo | | 丛书名称 | Statistics and Computing | | 图书封面 |  | | 描述 | Every advance in computer architecture and software tempts statisticians to tackle numerically harder problems. To do so intelligently requires a good working knowledge of numerical analysis. This book equips students to craft their own software and to understand the advantages and disadvantages of different numerical methods. Issues of numerical stability, accurate approximation, computational complexity, and mathematical modeling share the limelight in a broad yet rigorous overview of those parts of numerical analysis most relevant to statisticians.In this second edition, the material on optimization has been completely rewritten. There is now an entire chapter on the MM algorithm in addition to more comprehensive treatments of constrained optimization, penalty and barrier methods, and model selection via the lasso. There is also new material on the Cholesky decomposition, Gram-Schmidt orthogonalization, the QR decomposition, the singular value decomposition, and reproducing kernel Hilbert spaces. The discussions of the bootstrap, permutation testing, independent Monte Carlo, and hidden Markov chains are updated, and a new chapter on advanced MCMC topics introduces students to Ma | | 出版日期 | Textbook 2010Latest edition | | 关键词 | Algorithm; Computational statistics; Monte Carlo sampling; Numerical analysis; Optimization; expectation– | | 版次 | 2 | | doi | https://doi.org/10.1007/978-1-4419-5945-4 | | isbn_softcover | 978-1-4614-2612-7 | | isbn_ebook | 978-1-4419-5945-4Series ISSN 1431-8784 Series E-ISSN 2197-1706 | | issn_series | 1431-8784 | | copyright | Springer Science+Business Media, LLC 2010 |
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发表于 2025-3-21 16:26:25
