A Note on an Alternative Outlier Model
Journal of the Royal Statistical Society Series B (Statistical Methodology)Published 1 July 1982Open access
R. Dennis Cook, Norton Holschuh, Sanford Weisberg
Citations41
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
Summary This paper examines modelling a single outlier in the normal theory fixed effects linear model as arising from an unknown observation with inflated variance. The maximum likelihood estimates are characterized in terms of standard least squares statistics. The estimated position of the outlier does not necessarily agree with the estimated position under the usual mean slippage outlier model, and an example where they differ is presented. A sufficient and common condition for agreement is given.
Keywords
Decision SciencesBiochemistry, Genetics and Molecular Biology
IEEE Transactions on Automatic ControlA new look at the statistical model identification
50,732 Citations1974Hirotugu Akaike
Journal of the American Statistical AssociationMaximum Likelihood Approaches to Variance Component Estimation and to Related Problems
2,447 Citations1977David A. Harville
BiometrikaA Bayesian approach to some outlier problems
243 Citations1968George E. P. Box, George C. Tiao
The problem of outlying observations is considered from a Bayesian viewpoint and the linear model is considered, which assumes that a good observation is normally distributed about its mean with variance o.2, and a bad one is normal with the same mean but a larger variance.
TechnometricsTesting for a Single Outlier in Simple Linear Regression
69 Citations1973Gary L. Tietjen, R. W. Moore +1 more
BiometricsTesting for a Single Outlier from a General Linear Regression
50 Citations1976Jonas H. Ellenberg
It is shown that the three approaches to detection of outliers from the general linear model Y = Xbeta + mu are exactly equivalent.
TechnometricsOn the Accuracy of Bonferroni Significance Levels for Detecting Outliers in Linear Models
39 Citations1981R. Dennis Cook, P. Prescott
This work presents a relatively simple alternative method for assessing the accuracy of the first-order Bonferroni upper bound that can be applied to any linear model and is suitable for routine use.
