• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.21-32
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• 2010

Graphical regression is a paradigm for obtaining regression information using plots without model assumptions. The general goal of this approach is to find lowdimensional sufficient summary plots without loss of important information. Model assessments using residual plots are less likely to be successful in models that are not linear. As an alternative approach, marginal model plots provide a general graphical method for assessing the model. We apply the methods of graphical regression and model assessment using marginal model plots to the logistic regression model.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.213-217
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• 2003

In this paper we discuss graphical and diagnostic methods for logistic regression, in which the response is the number of successes in a fixed number of trials.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.1-10
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• 1999

When the component proportions in mixture experiments are restricted by lower and upper bounds multicollinearity appears all too frequently. The ridge regression can be used to stabilize the coefficient estimates in the fitted model. I propose a graphical method for evaluating the mixture component effects of ridge regression estimator with respect to the prediction variance and the prediction bias.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.189-195
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• 2009

A dynamic graphical procedure is suggested to estimate optimal response transformation parameter and a curvature function of covariates in the regression model. Augmented partial residual plot is chosen for specifying a curvature. The proposed method is compared with a different approach (Soo, 2007) and is investigated efficiency by applying it to the real and the artificial data. The method is also extended to the 3D graphical situations.

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

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.187-191
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• 2000

In the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. In this paper, we propose a simple graphical method to detect outliers and to investigate the goodness of model fit in repeated measures data. The graphical method is based on the quantile-quantile(Q-Q) plots of the $X^2$ distribution and the standard normal distribution. We also propose diagnostic measures to detect influential observations. The proposed method is illustrated using two examples.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.19-24
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• 1998

When multicollinearity appears in linear regression model, we can use ridge regression for stabilizing the regression coefficient estimates. We propose the screen diagram as a graphical method for detecting multicollinearity and estimating ridge constant in linear regression model.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.393-401
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• 2000

Many graphical methods have been suggested for obtaining an impression of a curvature in regression problem in which some covariates enter nonlinearly. However when true model does not belong to the class of additive models, graphical methods may contain a serious bias. A method is suggested which can avoid such bias in the fitting of nonaddive models.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.141-150
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• 2015

Graphical paradigms for assessing the adequacy of models in logistic regression are discussed. The residual plot has been widely used as a graphical tool for evaluating the adequacy of the model. However, this approach works well only for linear models with constant variance, and the alternative approach, the marginal model plot, has its defects as well. We suggest a Chi-residual plot that overcomes the potential shortcomings of the marginal model plot.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1191-1208
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• 2015

Most statistical methods are dependent on the summary statistic. However, with graphical approaches, it is easier to identify the characteristics of the data and detect information that cannot be obtained by the summary statistic. We present various graphical methods to assess the adequacy of models in logistic regression that include checking log-density ratio, structural dimension, marginal model plot, chi-residual plot, and CERES plot. Through simulation data, we investigate and compare the results of graphical approaches under diverse conditions.