An empirical comparison of some statistics for identifying outliers and influential observations in linear regression models

Balasooriya, Uditha; Tse, Yiu Kuen; Liew, Yuk San

doi:10.1080/02664768700000022

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…Although this requirement can be satisfied in some balanced factorial designs, it is not true in straight line regression. Prescott (1975) and Balasooriya et al (1987) use Stefansky's formula as an approximation-for detecting extreme values in linear regression and have obtained some satisfactory results.…”

Section: Literature Reviewmentioning

confidence: 91%

Coversge probabilty to the original data by prediction intervals in simple linear regression

Yang

1990

Journal of Statistical Computation and Simulation

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The coverage rate of the original data by the prediction interval in simple linear regression is obtained by computer simulation. The results show that for small sample size, the coverage rate is higher than the assigned prediction coverage rate (confidence level). The two coverage rates begin to converge when the sample size is larger than 50 and the convergence rate depends very little on the distribution of the independent variable. Also, theoretical results on the asymptotic coverage rate and on the absolute minimum bounds are obtained.

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Section: Literature Reviewmentioning

confidence: 91%

Coversge probabilty to the original data by prediction intervals in simple linear regression

Yang

1990

Journal of Statistical Computation and Simulation

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“…These data have great attention in the literature. 1,2,[50][51][52][53] All these studies used this data set for influence diagnostic purposes in the LM without testing the distribution of the response variable. Therefore, we thoroughly investigated the probability distribution of the response variable.…”

Section: Applicationsmentioning

confidence: 99%

Influence diagnostics in the inverse Gaussian ridge regression model: Applications in chemometrics

Amin

Faisal

Akram

2021

Journal of Chemometrics

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The influential observation affects the regression model inferences. Literature has shown that the problems of multicollinearity and influential observations can jointly exist in a model. The ridge regression estimator has been developed to handle the challenge of multicollinearity. The detection of influential observations with multicollinearity and its impact on the ridge estimates is necessary for better decision making. In this article, we proposed some influence diagnostics for the inverse Gaussian ridge regression model (IGRRM). The proposed diagnostics are evaluated with the help of a simulation study and two chemometric-related data sets. We found that the covariance ratio (CVR) method is better than other methods for the detection of influential observations under smaller dispersion. While for larger dispersion, all the IGRRM diagnostics perform equally well for the identification of influential observations.

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“…The study of outliers in structured situations like regression models and designed experiments has been carried out by numerous authors including Gentleman and Wilk ([13], [14]), John and Draper [15], Prescott [16], and John [17] and are based on residuals. Balasooriya et al [18] carried out an empirical study to identify the best of seven commonly used methods for identifying outliers in linear regression models based on several data sets. The methods they compared are due to Tietjen et al [19], Prescott [16], Andrews and Pregibon [20], Cook and Weisberg [21], Cook [22], and Draper and John [23].…”

Section: Univariate Outliersmentioning

confidence: 99%

An Overview of Multiple Outliers in Multidimensional Data

Sajesh

Srinivasan

2013

Sri Lankan J App Stats

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The process of detection of outliers is an interesting and important aspect in the analysis of data, as it could impact the inference. Literature is abundant with procedures for detection and testing of single outliers in sample data. However, the presence of two or more outliers in multivariate data would render the detection and testing process more complicated as majority of outliers are invisible to many of the methods. This is due to the masking effect, and regular classical and related methods being found unsuitable for use of outlier identification techniques. The difficulty of detection increases with the number of outliers and the dimension of the data because the outliers can be extreme in any growing number of directions. An overview of multivariate outlier detection methods are provided in this study because of its growing importance in a wide variety of practical situations.

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An empirical comparison of some statistics for identifying outliers and influential observations in linear regression models

Cited by 8 publications

References 15 publications

Coversge probabilty to the original data by prediction intervals in simple linear regression

Coversge probabilty to the original data by prediction intervals in simple linear regression

Influence diagnostics in the inverse Gaussian ridge regression model: Applications in chemometrics

An Overview of Multiple Outliers in Multidimensional Data

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