• Journal of the Korean Statistical Society
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• v.20 no.2
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• pp.177-190
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• 1991

In this paper, we discuss and review various measures which have been presented for studying outliers. high-leverage points, and influential observations when principal component regression is adopted. We suggest several diagnostics measures when principal component regression is used. A numerical example is illustrated. Some individual data points may be flagged as outliers, high-leverage point, or influential points.

• Journal of applied mathematics & informatics
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• v.13 no.1_2
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• pp.479-485
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• 2003

The influence of observations on the Wilks' lambda test of a linear hypothesis in multivariate regression is investigated using the local influence method. The perturbation scheme of case-weights is considered. A numerical example is given to show the effectiveness of the local influence method in identifying the influential observations.

• Journal of applied mathematics & informatics
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• v.22 no.1_2
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• pp.541-547
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• 2006

A procedure for investigating the influence of observations in choosing Box-Cox transformation for multivariate data is suggested. It is effective in spotting influential observations. A numerical example is provided for illustration.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.659-666
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• 1999

In comparing two population means, the test statistic depends on the sample means and the variances, which are very sensitive to the extremely large or small values. This paper aims at examining the behavior of such observations using proper criterion which can measure the influence of them. We derive a computationally feasible statistic which can detect influential observations on the two-sample t-statistic.

• Communications of Mathematical Education
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• v.5
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• pp.513-522
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• 1997

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.155-162
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• 2017

In this paper we propose influence measures for two basic goodness-of-fit statistics, the coefficient of determination $R^2$ and test statistic F in the linear regression model using the deletion method. Some useful lemmas are provided. We also express the influence measures in terms of basic building blocks such as residual, leverage, and deviation that showed them as increasing function of residuals and a decreasing function of deviation. Further, the proposed measure reduces computational burden from O(n) to O(1). As illustrative examples, we applied the proposed measures to the stackloss data sets. We verified that deletion of one or few influential observations may result in big change in $R^2$ and F-statistic.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.145-151
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• 1996

The local behaviour of the surface formed by the perturbed maximum likelihood estimator of the squared Mahalanobis distance is investigated. The study of the local behaviour allows a simultaneous perturbation on the samples of interest and it is effective in identifying influential observations.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.33-42
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• 1999

A method of detecting influential observations in principal factor analysis is suggested. it is based on a perturbation of the empirical distribution function and an adoption of the local influence method. An illustrative example is given.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.401-411
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• 2007

In this paper we propose an influence measure for detecting potentially influential observations using the infinitesimal perturbation and the local influence in the log-linear regression model. Also, we propose a goodness-of-fit measure for variable selection. A real data set are used for illustration.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.139-151
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• 2001

Although lots of works are done in influence diagnostics, results in the multivariate analysis are quite rare. One of recent works done by Fung(1995) is about the single case influence diagnostics in the linear discriminant analysis. In this paper we extend Fung's results to the multiple cases diagnostics which are necessary in the linear discriminant analysis for two reasons among others; First, the masking effect cannot be detected by single case diagnostics and secondly two populations are concerned in the discriminant analysis, i.e., influential cases can occur in one or both populations.