scholarly journals Information for Authors: Is the Advice Regarding the Reporting of Residuals in Regression Analysis Incomplete? Should Cook’s Distance Be Included?

2006 ◽  
Vol 52 (10) ◽  
pp. 1848-1850 ◽  
Author(s):  
A Ralph Henderson
Keyword(s):  
Regression Analysis ◽  
Cook’S Distance ◽  
Cook's Distance

scholarly journals OUTLIER SENSITIVITY ON THE SEA EXTREMES BY THE TEMPORAL AND CLIMATE INDEX COVARIATIONS

2012 ◽  
Vol 1 (33) ◽  
pp. 3
Author(s):  
Toshikazu Kitano ◽  
Wataru Kioka ◽  
Rinya Takahashi
Keyword(s):  
Regression Analysis ◽  
Outlier Detection ◽  
Total Variation ◽  
Exponential Family ◽  
Classical Problem ◽  
Extreme Value Distribution ◽  
Climate Index ◽  
Cook’S Distance ◽  
Cook's Distance

Outlier detection is one of the classical problem in the regression analysis. For this purpose the Cook's distance was proposed as the amount of changing the predictions by removing the candidate outlier in comparison with the total variation of the residuals against the fitting plane. This distance is considered to be so useful that it is rearranged and discribed in the two terms of the leverage of covariates and the contingent discrepancy. Hence the outlier detection can be displayed as a diagram with these two terms. Extremes generally accompanies outliers. Unfortunately the Cook's distance wouldn't be applicable to the outlier among the extremes. It is one of the reason that the extreme value distribution doesn't belong to the exponential family. Thus we should find the alternative way. The degree of experience, proposed originally for evaluating the limitation of extrapolation, will play an important role of detecting the outliers, because it is decomposed into two parts of the leverage of covariates and the contingent discrepancy in the average sense. Not only the mathematical derivations are shown but also a practical judgement for the removal of outliers is demonstrated in a diagram of leverage and residual of extremes.

Reference values for cook's distance

1996 ◽  
Vol 25 (3) ◽  
pp. 691-708 ◽  
Author(s):  
Choongrak Kim ◽  
Barry E. storer
Keyword(s):  
Reference Values ◽  
Cook’S Distance ◽  
Cook's Distance

scholarly journals Deletion statistic accuracy in confirmatory factor models

2020 ◽  
Vol 13 (2) ◽  
pp. 205979912091834
Author(s):  
Jennifer Koran ◽  
Fathima Jaffari
Keyword(s):  
Factor Model ◽  
Model Misspecification ◽  
Statistical Simulation ◽  
Factor Models ◽  
Case Deletion ◽  
Difference Statistic ◽  
Confirmatory Factor ◽  
Cook’S Distance ◽  
Cook's Distance ◽  
Influential Cases

Social science researchers now routinely use confirmatory factor models in scale development and validation studies. Methodologists have known for some time that the results of fitting a confirmatory factor model can be unduly influenced by one or a few cases in the data. However, there has been little development and use of case diagnostics for identifying influential cases with confirmatory factor models. A few case deletion statistics have been proposed to identify influential cases in confirmatory factor models. However, these statistics have not been systematically evaluated or compared for their accuracy. This study evaluated the accuracy of three case deletion statistics found in the R package influence.SEM. The accuracy of the case deletion statistics was also compared to Mahalanobis distance, which is commonly used to screen for unusual cases in multivariate applications. A statistical simulation was used to compare the accuracy of the statistics in identifying target cases generated from a model in which variables were uncorrelated. The results showed that Likelihood distance and generalized Cook’s distance detected the target cases more effectively than the Chi-square difference statistic. The accuracy of the Likelihood distance and generalized Cook’s distance statistics was unaffected by model misspecification. The results of this study suggest that Likelihood distance and generalized Cook’s distance are more accurate under more varied conditions in identifying target cases in confirmatory factor models.

scholarly journals Detection of Influential Observations in Spatial Regression Model Based on Outliers and Bad Leverage Classification

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2030
Author(s):  
Ali Mohammed Baba ◽  
Habshah Midi ◽  
Mohd Bakri Adam ◽  
Nur Haizum Abd Rahman
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Spatial Regression ◽  
Spatial Models ◽  
Influential Observations ◽  
Leverage Points ◽  
Cook’S Distance ◽  
Spatial Regression Models ◽  
Cook's Distance ◽  
Classical Regression

Influential observations (IOs), which are outliers in the x direction, y direction or both, remain a problem in the classical regression model fitting. Spatial regression models have a peculiar kind of outliers because they are local in nature. Spatial regression models are also not free from the effect of influential observations. Researchers have adapted some classical regression techniques to spatial models and obtained satisfactory results. However, masking or/and swamping remains a stumbling block for such methods. In this article, we obtain a measure of spatial Studentized prediction residuals that incorporate spatial information on the dependent variable and the residuals. We propose a robust spatial diagnostic plot to classify observations into regular observations, vertical outliers, good and bad leverage points using a classification based on spatial Studentized prediction residuals and spatial diagnostic potentials, which we refer to as and . Observations that fall into the vertical outliers and bad leverage points categories are referred to as IOs. Representations of some classical regression measures of diagnostic in general spatial models are presented. The commonly used diagnostic measure in spatial diagnostics, the Cook’s distance, is compared to some robust methods, (using robust and non-robust measures), and our proposed and plots. Results of our simulation study and applications to real data showed that the Cook’s distance, non-robust and robust were not very successful in detecting IOs. The suffered from the masking effect, and the robust suffered from swamping in general spatial models. Interestingly, the results showed that the proposed plot, followed by the plot, was very successful in classifying observations into the correct groups, hence correctly detecting the real IOs.

Multivariate Outliers: A Conceptual and Practical Overview for the Nurse and Health Researcher

2020 ◽  
pp. 084456212093205
Author(s):  
Maher M. El-Masri ◽  
Fabrice I. Mowbray ◽  
Susan M. Fox-Wasylyshyn ◽  
David Kanters
Keyword(s):  
Mahalanobis Distance ◽  
Statistical Models ◽  
Parameter Estimates ◽  
Outlier Identification ◽  
Health Researcher ◽  
Multivariate Outliers ◽  
Cook’S Distance ◽  
Research Findings ◽  
Identification Techniques ◽  
Cook's Distance

The presence of statistical outliers is a shared concern in research. If ignored or improperly handled, outliers have the potential to distort parameter estimates and possibly compromise the validity of research findings. The purpose of this paper is to provide a conceptual and practical overview of multivariate outliers with a focus on common techniques used to identify and manage multivariate outliers. Specifically, this paper discusses the use of Mahalanobis distance and residual statistics as common multivariate outlier identification techniques. It also discusses the use of leverage and Cook’s distance as two common techniques to determine the influence that multivariate outliers may have on statistical models. Finally, this paper discusses techniques that are commonly used to handle influential multivariate outlier cases.

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