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Titlebook: Identification of Outliers; D. M. Hawkins Book 1980 D. M. Hawkins 1980 Area.Factor.Flit.Volume.computation.distribution.identification.sta

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發(fā)表于 2025-3-21 16:36:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Identification of Outliers
編輯D. M. Hawkins
視頻videohttp://file.papertrans.cn/461/460841/460841.mp4
叢書名稱Monographs on Statistics and Applied Probability
圖書封面Titlebook: Identification of Outliers;  D. M. Hawkins Book 1980 D. M. Hawkins 1980 Area.Factor.Flit.Volume.computation.distribution.identification.sta
描述The problem of outliers is one of the oldest in statistics, and during the last century and a half interest in it has waxed and waned several times. Currently it is once again an active research area after some years of relative neglect, and recent work has solved a number of old problems in outlier theory, and identified new ones. The major results are, however, scattered amongst many journal articles, and for some time there has been a clear need to bring them together in one place. That was the original intention of this monograph: but during execution it became clear that the existing theory of outliers was deficient in several areas, and so the monograph also contains a number of new results and conjectures. In view of the enormous volume ofliterature on the outlier problem and its cousins, no attempt has been made to make the coverage exhaustive. The material is concerned almost entirely with the use of outlier tests that are known (or may reasonably be expected) to be optimal in some way. Such topics as robust estimation are largely ignored, being covered more adequately in other sources. The numerous ad hoc statistics proposed in the early work on the grounds of intuitive a
出版日期Book 1980
關(guān)鍵詞Area; Factor; Flit; Volume; computation; distribution; identification; statistics; testing
版次1
doihttps://doi.org/10.1007/978-94-015-3994-4
isbn_softcover978-94-015-3996-8
isbn_ebook978-94-015-3994-4
copyrightD. M. Hawkins 1980
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 21:48:50 | 只看該作者
General theoretical principles,In Chapter 1, a distinction was made between situations in which our primary concern is to estimate one or more parameters of the underlying distribution, and that in which we wish to classify the data into ‘good’ data and outliers (if any).
板凳
發(fā)表于 2025-3-22 00:55:55 | 只看該作者
The gamma distribution,The general form of the gamma distribution is . The parameter α is a shape parameter and ? a scale parameter.
地板
發(fā)表于 2025-3-22 07:10:29 | 只看該作者
Outliers from the linear model,The standard form of the general linear model is . where the . × 1 vector . represents the independent variables measured, the . matrix . is the design matrix, and . the . × 1 vector of unknown regression coefficients. The error vector . is assumed to consist of . independent, identically distributed N(0, ..) variables.
5#
發(fā)表于 2025-3-22 11:05:01 | 只看該作者
Bayesian approach to outliers,We have already mentioned the suggestion by Glaisher that the usual arithmetic mean was not appropriate for observations having a common mean, but different precisions.
6#
發(fā)表于 2025-3-22 15:20:50 | 只看該作者
Miscellaneous topics,The general procedure for testing for outliers in a continuous distribution with unknown parameters is: find a suitable (e.g. complete sufficient) statistic . for the parameters, and then find a suitable outlier test statistic . whose distribution does not depend on the unknown parameters.
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發(fā)表于 2025-3-22 20:06:08 | 只看該作者
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發(fā)表于 2025-3-22 23:29:26 | 只看該作者
9#
發(fā)表于 2025-3-23 01:26:21 | 只看該作者
Non-parametric tests, is really not surprising. Most of the results that there are relate to slippage rather than outlier problems. Suppose that we are given .., . = 1 to .., . = 1 to . = ∑ .. observations from . groups. Let .. denote an arbitrary observation from population ..
10#
發(fā)表于 2025-3-23 07:03:01 | 只看該作者
Introduction,er would be ‘a(chǎn)n observation which deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism’. An inspection of a sample containing outliers would show up such characteristics as large gaps between ‘outlying’ and ‘inlying’ observations and the dev
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