Identification of OutliersSpringer Science & Business Media, 17 de abr. de 2013 - 188 páginas 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 appeal or computational simplicity also are not discussed in any detail. |
Conteúdo
| 1 | |
General theoretical principles | 13 |
A single outlier in normal samples | 27 |
Multiple outliers 51 | 35 |
The gamma distribution | 42 |
Nonparametric tests | 74 |
Outliers from the linear model | 85 |
Multivariate outlier detection | 104 |
Miscellaneous topics | 123 |
Bibliography | 128 |
Fractiles of B and B for normal samples | 136 |
Fractiles of Er for normal samples | 144 |
Fractiles for testing for two outliers | 152 |
Fractiles of the Wilks statistics | 159 |
A single outlier in a twoway factorial | 174 |
| 185 | |
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Termos e frases comuns
a/n fractile a₁ alternative analysis of variance Annals of Mathematical Appendix approach assumed asymptotic b₁ backward elimination basic distribution Bayesian beta distribution binomial Biometrika Bonferroni approximation Bonferroni's inequality c₁ Chapter computed cumulative distribution function degrees of freedom denote density detection Doornbos estimate forward selection fractiles gamma gamma distribution given H₁ Hawkins inliers known largest Mathematical Statistics matrix Mosteller multiple comparison multiple outliers multivariate n₁ normal distribution normal samples null hypothesis number of outliers optimal test order statistics outlier problem outlier statistic outlier test outliers are present outlying observations parameters principal component residuals probability procedure R₁ recursive residuals regression robust S₁ shows significance level simulation single outlier situation slippage problem slippage test ẞ₁ Student's t-distribution sufficient statistic Suppose t-distribution T₁ Technometrics test statistic theory tolerance regions unknown values variables X₁ β₁ βι σ₁ σ² Χ₁
