• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.787-797
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• 2002

Partial residual plots, augmented partial residual plots and CERES plots are basic diagnostic tools for dealing with curvature as a function of specific predictors in regression problem. However, it is known that these plots can miss a curve or show a false curve in some cases such as predictors are related each other. Dynamic display of these plots is developed and applied. Examples demonstrate that dynamic plots are useful for obtaining additional Information on the curvature.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.529-537
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• 2004

This article concerns dynamic graphical methods for visualizing a curvature in regression problem in which some predictors enter nonlinearly. A sequence of augmented partial residual plot or partial residual plot updated by the change of linear combination of two predictors are constructed. Examples demonstrate that the suggested methods can be used to reduce the dimension of explanatory variables as well as to capture a curvature.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.311-317
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• 2001

It is necessary to check the linearity of selected covariates in regression diagnostics. There are various graphical methods using residual plots such as partial residual plots, augmented partial residual plots and combining conditional expectation and residual plots. In this paper, we propose the modified pseudolikelihood ratio test statistics based on these residual plots to test linearity of selected covariate. These test statistics which measure the distance between the nonparametric and parametric models are derived as a ratio of quadratic forms. The approximate distribution of these statistics is calculated numerically by using three moments. The power comparison of these statistics is given.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.345-353
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• 2004

Graphical methods for the specification of the curvature as a function of two predictors are animated to see the effect of an added variable to the model. Through a 3D animated plot it might be difficult to find a sequence of interpretable plots. But examples demonstrate that useful information can be obtained by using rotation technique in 3D plot. Besides 3D plots, an example of 2D animated plot applied to the case of high correlation between predictors and an added predictor is also given. It implies that speed of the convergence to a certain image in a dynamic plot may be understood as an influence of collinearity.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.1-12
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• 2000

We explore the structure and usefulness of CERES plot as a basic tool for dealing with curvature as a function of the new predictor in nonlinear regression. If a predictor has a nonlinear effect and there are nonlinear relationships among the predictors the partial residual plot and augmented partial residual plot are not able to display the correct functional form of the predictor. Unlike these plots the CERES plot can show the correct from. In situations where nonlinearity exists in two predictors we extend the idea of CERES plot to three dimensions, This is illustrated by simulated data.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.575-582
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• 2004

We explore the structure and usefulness of CERES plot as a basic tool for dealing with curvature as a function of the new predictor in generalized linear models. If a predictor has a nonlinear effect and there are nonlinear relationships among the predictors, the partial residual plot and augmented partial residual plot are not able to display the correct functional form of the predictor. Unlike these plots, the CERES plot can show the correct form. This is illustrated by simulated data.