• Journal of Korean Academy of Oral and Maxillofacial Radiology
• /
• v.28 no.2
• /
• pp.415-433
• /
• 1998

Digora system is an intraoral indirect digital radiography system utilizing storage phosphor image plate. It has wide dynamic range which allows it to decrease the patient s exposure time and may increase diagnostic ability through image processing (such as edge enhancement, grey scale conversion, brightness change, and contrast enhancement). And also, it can transmit and storage image information. The purpose of this study was to evaluate the diagnostic ability of artificial proximal caries between Conventional radiograph and Digora images(unenhanced image, brightness & contrast controlled image, and edge enhanced image). ROC(Receiver Operating Characteristic) analysis, paired t-tests, and F-tests were done for the statistical evaluation of detectability. The following results were acquired: 1. In Grade I lesions, the mean ROC areas of Conventional radiograph, Digora unenhanced image, Digora controlled image, and Digora edge enhanced image were 0.953, 0.933, 0.965, 0.978 (p>0.05). 2. In Grade II lesions, the mean ROC areas of Conventional radiograph, Digora unenhanced image, Digora controlled image, and Digora edge enhanced image were 0.969, 0.964, 0.988, 0.994. Among theses areas, there was just statistical significance between Diagnostic abilities of Digora edge enhanced image and Conventional radiograph (p<0.05). 3. In the Interobserver variability, the ROC curve areas of Digora edge enhanced image was lowermost in these areas, regardless of the Carious lesion depths. In conclusion, intraoral indirect digital system, Digora system, has the potential possibility as an alternative of Conventional radiograph in the diagnosis of proximal caries.

• Journal of Korean Neurosurgical Society
• /
• v.52 no.6
• /
• pp.528-533
• /
• 2012

Objective : In the pediatric population the skull has not yet undergone ossification and it is assumed that the diagnostic rate of skull fractures by simple X-rays are lower than that of adults. It has been recently proposed that the diagnostic rates of skull fractures by 3-dimensional computer tomography (3D-CT) are higher than simple X-rays. The authors therefore attempted to compare the diagnostic rates of pediatric skull fractures by simple X-rays and 3D-CTs with respect to the type of fracture. Methods : One-hundred patients aged less than 12 years who visited the Emergency Center for cranial injury were subject to simple X-rays and 3D-CTs. The type and location of the fractures were compared and Kappa statistical analysis and the t-test were conducted. Results : Among the 100 pediatric patients, 65 were male and 35 were female. The mean age was $50{\pm}45$ months. 63 patients had simple skull fractures and 22 had complex fractures, and the types of fractures were linear fractures in 74, diastatic fractures 15, depressed fractures in 10, penetrating fracture in 1, and greenstick fractures in 3 patients. Statistical difference was observed for the predictive value of simple skull fractures' diagnostic rate depending on the method for diagnosis. A significant difference of the Kappa value was noted in the diagnosis of depressed skull fractures and diastatic skull fractures. Conclusion : In the majority of pediatric skull fractures, 3D-CT showed superior diagnosis rates compared to simple skull X-rays and therefore 3D-CT is recommended whenever skull fractures are suspected. This is especially true for depressed skull fractures and diastatic skull fractures.

• Communications for Statistical Applications and Methods
• /
• v.9 no.3
• /
• pp.787-797
• /
• 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
• /
• v.8 no.2
• /
• pp.311-317
• /
• 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.

• Journal of the Korean Statistical Society
• /
• v.31 no.2
• /
• pp.237-250
• /
• 2002

Motivated by Cook's (1986) assessment of local influence by investigating the curvature of a surface associated with the overall discrepancy measure, this paper extends this idea to the linear regression model with AR(p) disturbances. Diagnostic for the linear regression models with AR(p) disturbances are discussed when simultaneous perturbations of the response vector are allowed. For the derived criterion, numerical studies demonstrate routine application of this work.

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

• Journal of the Korean Statistical Society
• /
• v.22 no.1
• /
• pp.27-37
• /
• 1993

Since the analytic expression for a diagnostic in the logistic regression model is not available, one-step estimation is often used by a case-deletion point of view. In this paper, infinitesimal perturbation approach is used, and it is shown that the scale transformation of infinitesimal perturbation approach is eventually equal to the weighted perturbation of local influence approach and the replacement measure. Also, multiple cases deletion for the masking effect is considered.

• Communications for Statistical Applications and Methods
• /
• v.5 no.1
• /
• pp.55-67
• /
• 1998

Recent work by Shin and Sarkar (1996) examines model misspecification problems for nonstationary time series. Shin and Sarkar introduce a general regression model with integrated errors and one system of integrated regressors and discuss the limiting distributions of the OLS estimators and the usual OLS statistics such as $\hat{\sigma^2}$t, DW and $R^2$. We analyze three different model misspecification problems through a Monte Carlo study and investigate each model misspecification problem. Our Monte Carlo experiments show that DW and $R^2$ can be in general used as diagnostic tools to detect spurious regression, misspecification of nonstationary autoregressive and polynomial regression models.

• Journal of the Korean Statistical Society
• /
• v.21 no.1
• /
• pp.35-46
• /
• 1992

On Box-Cox transformation, one or few responses are influential on transformation parameter estimator. To detect influential observatins, several diagnostics (Cook and Wang 1983, Hinkley and Wang 1988, Lawrance 1988, Tsai and Wu 1990) have been suggested. We compare these diagnostics and denote the necessity of multiple cases deletion which is important especially when the masking effect is present. Also, analytic expression of Tsai and Wu's diagnostic is given. We suggest a computationally feasible and useful algorithm based on the basic building blocks, and present descriptive examples using artificial data.

• Communications for Statistical Applications and Methods
• /
• v.2 no.2
• /
• pp.296-302
• /
• 1995

When heteroscedasticity occurs in random effects linear model, the error variance may depend on the values of one or more of the explanatory variables or on other relevant quantities such as time or spatial ordering. In this paper we derive a score test as a diagnostic tool for detecting non-constant error variance in random effefts linear model based on the model expansion on error variance. This score test is compared to loglikelihood ratio test.