• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 1995.03a
• /
• pp.162-169
• /
• 1995

An improved nonparametric tool description based on successive refined monparametric patches is proposed and therlated criterion for refinement is also discussed . In the proposed sheme, any required order of tool surface conformity can be achieved by employing successive refinements accoring to the suggested criterion. By using the suggested adaptive tool refinement technique based on the nonparametric patch tool description, the locally refined nonparametric tool surface with economic memory size and sufficient accuracy as well as with favorable charateristics for contact treatment can be obtained directly form the parametric patch related with commerical CAD system. Computation is carried out for a chosen complex sheet forming example of an actual autobody panel in order to verify the validity and the efficiency of the developed tool surface description.

• Communications for Statistical Applications and Methods
• /
• v.19 no.5
• /
• pp.721-729
• /
• 2012

Various methods control the influence of a covariate on a response variable. These methods are analysis of covariance(ANCOVA), RANK ANCOVA, ANOVA of (covariate-adjusted) residuals, and Kruskal-Wallis tests on residuals. Covariate-adjusted residuals are obtained from the overall regression line fit to the entire data set that ignore the treatment levels or factors. It is demonstrated that the methods on covariate-adjusted residuals are only appropriate when the regression lines are parallel and covariate means are equal for all treatments. In this paper, we proposed the new nonparametric method on the ANCOVA model, as applying joint placement in a one-way layout on residuals as described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed procedure with those of the previous procedure.

• Communications for Statistical Applications and Methods
• /
• v.27 no.3
• /
• pp.365-376
• /
• 2020

Interval-valued data, a type of symbolic data, is given as an interval in which the observation object is not a single value. It can also occur frequently in the process of aggregating large databases into a form that is easy to manage. Various regression methods for interval-valued data have been proposed relatively recently. In this paper, we introduce a nonparametric regression model using the kernel function and a nonlinear regression model for the interval-valued data. We also propose applying the local linear regression model, one of the nonparametric methods, to the interval-valued data. Simulations based on several distributions of the center point and the range are conducted using each of the methods presented in this paper. Various conditions confirm that the performance of the proposed local linear estimator is better than the others.

• Journal of the Korean Data and Information Science Society
• /
• v.16 no.4
• /
• pp.1129-1140
• /
• 2005

A nonparametric Bayesian method for calculating posterior probabilities of the multiple comparison problem on the parameters of several Geometric populations is presented. Bayesian multiple comparisons under two different prior/ likelihood combinations was studied by Gopalan and Berry(1998) using Dirichlet process priors. In this paper, we followed the same approach to calculate posterior probabilities for various hypotheses in a statistical experiment with a partition on the parameter space induced by equality and inequality relationships on the parameters of several geometric populations. This also leads to a simple method for obtaining pairwise comparisons of probability of successes. Gibbs sampling technique was used to evaluate the posterior probabilities of all possible hypotheses that are analytically intractable. A numerical example is given to illustrate the procedure.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.6
• /
• pp.1401-1408
• /
• 2013

Kim and Kim (1992) proposed typical nonparametric method for umbrella alternative in randomized block design with replications. In this paper, We consider a test procedure for umbrella alternatives in a randomized block design using extension of the two sample placement tests described in Orban and Wolfe (1982) and treatment tests described in Kim (1999). We perform a Monte Carlo study to compare the empirical powers of the test statistics for underlying distributions.

• Communications for Statistical Applications and Methods
• /
• v.19 no.6
• /
• pp.781-791
• /
• 2012

A $2{\times}2$ Cross-over design is widely used in clinical trials for comparison studies of two kinds of drugs or medical treatments. This design has many statistical methods such as Hills-Armitage's (1979) method or Koch's (1972) method. In this paper, we propose a nonparametric test for $2{\times}2$ Cross-over design based on a two-sample test suggested by Baumgartner et al. (1998). In addition, a Monte Carlo simulation study is adapted to compare the power of the proposed methods with those of previous methods.

• Communications for Statistical Applications and Methods
• /
• v.27 no.5
• /
• pp.547-568
• /
• 2020

Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by computing the empirical variogram (traditional Matheron or robust Cressie-Hawkins or kernel-based nonparametric approaches). In this article, we conduct numerical studies comparing the performance of these empirical variograms. In most situations, the nonparametric empirical variable nearest-neighbor (VNN) showed better performance than its competitors (Matheron, Cressie-Hawkins, and Nadaraya-Watson). The analysis of the spatial groundwater dataset used in this article suggests that the wave variogram model, with hole effect structure, fitted to the empirical VNN variogram is the most appropriate choice. This selected variogram is used with the ordinary kriging model to produce the predicted pollution map of the nitrate concentrations in groundwater dataset.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.23 no.4
• /
• pp.796-804
• /
• 1998

Multiplicative noise is known to be useful in modeling multipath propagation, which is crucial in mobile communication systems analysis. In this paper, nonparametric detection of weak random signals in multiplicative noise is considered. The locally optimum detector based on signs and ranks of observations isderived for good weak-signal detection performance under any noise probability density function. the detector has similarities to the locally optimum detector for random signals in multiplicative noise. It is shown that the nonparametric detector asymptotically hs almost the same performance as the locally optimum detector.

• International Journal of CAD/CAM
• /
• v.9 no.1
• /
• pp.31-36
• /
• 2010

3D point data acquired from laser scan or stereo vision can be quite noisy. A preprocessing step is often needed before a surface reconstruction algorithm can be applied. In this paper, we propose a nonparametric approach for noisy point data preprocessing. In particular, we proposed an anisotropic kernel based nonparametric density estimation method for outlier removal, and a hill-climbing line search approach for projecting data points onto the real surface boundary. Our approach is simple, robust and efficient. We demonstrate our method on both real and synthetic point datasets.

• 한국데이터정보과학회:학술대회논문집
• /
• 2006.11a
• /
• pp.71-80
• /
• 2006

A nonparametric Bayesian multiple comparisons problem (MCP) for dependence parameters in I bivariate exponential populations is studied here. A simple method for pairwise comparisons of these parameters is also suggested. Here we extend the methodology studied by Gopalan and Berry (1998) using Dirichlet process priors. The family of Dirichlet process priors is applied in the form of baseline prior and likelihood combination to provide the comparisons. Computation of the posterior probabilities of all possible hypotheses are carried out through Markov Chain Monte Carlo method, namely, Gibbs sampling, due to the intractability of analytic evaluation. The whole process of MCP for the dependent parameters of bivariate exponential populations is illustrated through a numerical example.