| 书目名称 | Least Absolute Deviations | | 副标题 | Theory, Applications | | 编辑 | Peter Bloomfield,William L. Steiger | | 视频video | | | 丛书名称 | Progress in Probability | | 图书封面 |  | | 描述 | Least squares is probably the best known method for fitting linear models and by far the most widely used. Surprisingly, the discrete L 1 analogue, least absolute deviations (LAD) seems to have been considered first. Possibly the LAD criterion was forced into the background because of the com putational difficulties associated with it. Recently there has been a resurgence of interest in LAD. It was spurred on by work that has resulted in efficient al gorithms for obtaining LAD fits. Another stimulus came from robust statistics. LAD estimates resist undue effects from a feyv, large errors. Therefore. in addition to being robust, they also make good starting points for other iterative, robust procedures. The LAD criterion has great utility. LAD fits are optimal for linear regressions where the errors are double exponential. However they also have excellent properties well outside this narrow context. In addition they are useful in other linear situations such as time series and multivariate data analysis. Finally, LAD fitting embodies a set of ideas that is important in linear optimization theory and numerical analysis. viii PREFACE In this monograph we will present a unified treat | | 出版日期 | Book 1983 | | 关键词 | algorithms; linear optimization; numerical analysis; optimization; statistics | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4684-8574-5 | | isbn_softcover | 978-1-4684-8576-9 | | isbn_ebook | 978-1-4684-8574-5Series ISSN 1050-6977 Series E-ISSN 2297-0428 | | issn_series | 1050-6977 | | copyright | Springer Science+Business Media New York 1983 |
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发表于 2025-3-21 19:07:42
