• Journal of Korean Society for Quality Management
• /
• v.19 no.2
• /
• pp.1-16
• /
• 1991

Recently, Cook and Weisberg(l989) presented dynamic graphics for regression diagnostics. They suggested animating graphics which could aid to understanding the effects of adding a variable to a model. In this paper, using the Cook and Weisberg's idea of animation, we propose a dynamic graphical method for residuals to display the effects of removing an observation from a model. Based on the information obtained from these animating graphics, it is possible to see the influence of outliers on influencial observations for regression diagnostics.

• Communications for Statistical Applications and Methods
• /
• v.4 no.1
• /
• pp.21-32
• /
• 1997

Robust regression based on M-estimator reduces and/or bounds the influence of outliers in the y-direction only. Therefore, when several influential observations exist, diagnostics in the robust regression is required in order to detect them. In this paper, we propose influence diagnostics in the robust regression based on M-estimator and its one-step version. Noting that M-estimator can be obtained through iterative weighted least squares regression by using internal weights, we apply the weighted least squares (WLS) regression diagnostics to robust regression.

• The Korean Journal of Applied Statistics
• /
• v.10 no.2
• /
• pp.241-251
• /
• 1997

Several studies have bee down on the work of dynamic graphical methods for regression diagnostics. The main propose of the methods were to investigate (1) the effects of change of data, or (2) the effects of change of regression coefficients on the regression models. But, by contrast, we can also investigate the effects of change of regression residuals on the regression model. This method can be used in fitting better a certain set of observations to a regression model than the other observations. Our research team approaches regression diagnostics by using dynamic graphics (REDS), and we introduce REDS in this thesis.

• Communications for Statistical Applications and Methods
• /
• v.7 no.1
• /
• pp.23-36
• /
• 2000

This paper discusses the local influence approach to the Weibull regression model with censored data. Diagnostics for the Weibull regression model are proposed and developed when simultaneous perturbations of the response vector are allowed.

• Journal of the Korean Data and Information Science Society
• /
• v.11 no.1
• /
• pp.73-81
• /
• 2000

The variance inflation factor can be expressed by the square of the ratio of t-statistics associated with slopes of partial regression and partial residual plots. Disagreement of two sides in the interpretation can be occurred, and we analyze it with some illustrations.

• Communications for Statistical Applications and Methods
• /
• v.16 no.2
• /
• pp.309-315
• /
• 2009

We extended the results of Roy and Guria (2008) to multiple deletions in logistic regression models. Since single deletions may not exactly detect outliers or influential observations due to swamping effects and masking effects, it needs multiple deletions. We developed conditional deletion diagnostics which are designed to overcome problems of masking effects. We derived the closed forms for several statistics in logistic regression models. They give useful diagnostics on the statistics.

• The Korean Journal of Applied Statistics
• /
• v.15 no.2
• /
• pp.233-250
• /
• 2002

Cox's proportional hazard model is highly-used for the regression analysis of survival data in various fields. Regression diagnostics for the proportional hazards model, however, is not as well-known as the diagnostics for the classical linear models and so these diagnostic methods are not used widely in our practical data analyses. For this reason, we review the residuals proposed by several authors, and investigate how to use them in assessing the model. We also provide the results and interpretation with the analysis of PBC data using S-plus 2000 program.

• The Pure and Applied Mathematics
• /
• v.27 no.2
• /
• pp.71-81
• /
• 2020

Multicollinearity is a common problem in linear regression models when two or more regressors are highly correlated, which yields some serious problems for the ordinary least square estimates of the parameters as well as model validation and interpretation. In this paper, first the problem of multicollinearity and its subsequent effects on the linear regression along with some important measures for detecting multicollinearity is reviewed, then the role of eigenvalues and eigenvectors in detecting multicollinearity are bolded. At the end a real data set is evaluated for which the fitted linear regression models is investigated for multicollinearity diagnostics.

• Journal of the Korean Statistical Society
• /
• v.19 no.2
• /
• pp.151-159
• /
• 1990

Regression diagnostics often involves assesment of the changes that result from deleting multiple cases. Diagnostic mehtodology based on global influence measure, however, needs prohibitive computing time. As an alternative, Cook (1986) developed influence approach in which it is checked whether a minor modification of specifiation influences key results of an analysis. In line with Cook's development, we propose and study an inflence derivative method that yields both the magnitude and direction of case influences. The utility of our methodology is highlighted when case influence derivatives are plotted in a lower demensional space. Such plots are especially effective in unmasking "masked" observations in least squares regression and in robust regression also. We give several illustrations.strations.

• Communications for Statistical Applications and Methods
• /
• v.3 no.2
• /
• pp.21-28
• /
• 1996

Collinearities contained in MEM cause the same problems as they do in traditional regression model, so the detection of collinearities is a crucial topic in MEM. One diagnostic was introduced by Carrillo-Gamboa and Gunst, but their method did not work in some cases. Two alternative collinearity diagnostics that provide reasonable measure of collinearities are proposed. Simulation study is performed to compare the small-sample properties of the proposed collinearity diagnostics.