Go to the main menu
Skip to content
Go to bottom
REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
Communications for Statistical Applications and Methods
Journal Basic Information
Journal DOI :
The Korean Statistical Society
Editor in Chief :
Volume & Issues
Volume 19, Issue 6 - Nov 2012
Volume 19, Issue 5 - Sep 2012
Volume 19, Issue 4 - Jul 2012
Volume 19, Issue 3 - May 2012
Volume 19, Issue 2 - Mar 2012
Volume 19, Issue 1 - Jan 2012
Selecting the target year
Fuzzy k-Means Local Centers of the Social Networks
Woo, Won-Seok ; Huh, Myung-Hoe ;
Communications for Statistical Applications and Methods, volume 19, issue 2, 2012, Pages 213~217
DOI : 10.5351/CKSS.2012.19.2.213
Fuzzy k-means clustering is an attractive alternative to the ordinary k-means clustering in analyzing multivariate data. Fuzzy versions yield more natural output by allowing overlapped k groups. In this study, we modify a fuzzy k-means clustering algorithm to be used for undirected social networks, apply the algorithm to both real and simulated cases, and report the results.
Influences of Dependence Degrees of a Component for the Mean Time to Failure of a System
Kim, Dae-Kyung ; Oh, Ji-Eun ;
Communications for Statistical Applications and Methods, volume 19, issue 2, 2012, Pages 219~224
DOI : 10.5351/CKSS.2012.19.2.219
This article considers the mean time to failure(MTTF) of a dependent parallel system. We study how the degree of dependency components influences the increase in the mean lifetime for this system. The results are illustrated by tables and figures.
Adaptive Noise Reduction Algorithm for Image Based on Block Approach
Kim, Yeong-Hwa ;
Communications for Statistical Applications and Methods, volume 19, issue 2, 2012, Pages 225~235
DOI : 10.5351/CKSS.2012.19.2.225
Noise reduction is an important issue in the field of image processing because image noise worsens the quality of the input image. The basic difficulty is that the noise and the signal are not easy to distinguish. Simple moothing is one of the most basic and important procedures to remove the noise, however, it does not consider the level of noise. This method effectively reduces the noise but the feature area is simultaneously blurred. This paper considers the block approach to detect noise and image features of the input image so that noise reduction could be adaptively applied. Simulation results show that the proposed algorithm improves the overall quality of the image by removing the noise according to the noise level.
Bayesian Multiple Change-Point for Small Data
Cheon, Soo-Young ; Yu, Wenxing ;
Communications for Statistical Applications and Methods, volume 19, issue 2, 2012, Pages 237~246
DOI : 10.5351/CKSS.2012.19.2.237
Bayesian methods have been recently used to identify multiple change-points. However, the studies for small data are limited. This paper suggests the Bayesian noncentral t distribution change-point model for small data, and applies the Metropolis-Hastings-within-Gibbs Sampling algorithm to the proposed model. Numerical results of simulation and real data show the performance of the new model in terms of the quality of the resulting estimation of the numbers and positions of change-points for small data.
On Complete Convergence for Weighted Sums of Pairwise Negatively Quadrant Dependent Sequences
Ko, Mi-Hwa ;
Communications for Statistical Applications and Methods, volume 19, issue 2, 2012, Pages 247~256
DOI : 10.5351/CKSS.2012.19.2.247
In this paper we prove the complete convergence for weighted sums of pairwise negatively quadrant dependent random variables. Some results on identically distributed and negatively associated setting of Liang and Su (1999) are generalized and extended to the pairwise negative quadrant dependence case.
WebER: Web Based Statistical Tool Interfacing R for Teaching Purposes
Ko, Young-Jun ; Park, Yong-Min ; Kim, Jin-Seog ;
Communications for Statistical Applications and Methods, volume 19, issue 2, 2012, Pages 257~266
DOI : 10.5351/CKSS.2012.19.2.257
R is a free software for statistical analysis that provides simple interfaces to other application programs. Many people are trying to learn R, but it is difficult to learn R compared to commercial software such as SPSS or SAS, and it is cumbersome to provide an environment to teach R. Thus, it is essential to provide a new web-based R environment for novice users or for laboratory use. We developedWebER (a web-based R environment) using PHP on the Linux apache server. WebER can be easily used by any R user because we implemented the same functions as the basic Rgui such as editing R program, generating the text, image outputs, errors and warnings. It is also possible for multi-users to access WebER.
Numerical Comparisons for the Null Distribution of the Bagai Statistic
Ha, Hyung-Tae ;
Communications for Statistical Applications and Methods, volume 19, issue 2, 2012, Pages 267~276
DOI : 10.5351/CKSS.2012.19.2.267
Bagai et al. (1989) proposed a distribution-free test for stochastic ordering in the competing risk model, and recently Murakami (2009) utilized a standard saddlepoint approximation to provide tail probabilities for the Bagai statistic under finite sample sizes. In the present paper, we consider the Gaussian-polynomial approximation proposed in Ha and Provost (2007) and compare it to the saddlepoint approximation in terms of approximating the percentiles of the Bagai statistic. We make numerical comparisons of these approximations for moderate sample sizes as was done in Murakami (2009). From the numerical results, it was observed that the Gaussianpolynomial approximation provides comparable or greater accuracy in the tail probabilities than the saddlepoint approximation. Unlike saddlepoint approximation, the Gaussian-polynomial approximation provides a simple explicit representation of the approximated density function. We also discuss the details of computations.
Bivariate ROC Curve
Hong, C.S. ; Kim, G.C. ; Jeong, J.A. ;
Communications for Statistical Applications and Methods, volume 19, issue 2, 2012, Pages 277~286
DOI : 10.5351/CKSS.2012.19.2.277
For credit assessment models, the ROC curves evaluate the classification performance using two univariate cumulative distribution functions of the false positive rate and true positive rate. In this paper, it is extended to two bivariate normal distribution functions of default and non-default borrowers; in addition, the bivariate ROC curves are proposed to represent the joint cumulative distribution functions by making use of the linear function that passes though the mean vectors of two score random variables. We explore the classification performance based on these ROC curves obtained from various bivariate normal distributions, and analyze with the corresponding AUROC. The optimal threshold could be derived from the bivariate ROC curve using many well known classification criteria and it is possible to establish an optimal cut-off criteria of bivariate mixture distribution functions.
Consistency of the Periodogram When the Long-Run Variance is Degenerate
Lee, Jin ;
Communications for Statistical Applications and Methods, volume 19, issue 2, 2012, Pages 287~292
DOI : 10.5351/CKSS.2012.19.2.287
Sample periodogram is widely known as an inconsistent estimator for true spectral density. We show that it becomes consistent when the true spectrum at the zero frequency (often known as long-run variance) equals zero. Asymptotic results for consistency of the periodogram as well as the rate of convergence are formally derived.
Wage Determinants Analysis by Quantile Regression Tree
Chang, Young-Jae ;
Communications for Statistical Applications and Methods, volume 19, issue 2, 2012, Pages 293~301
DOI : 10.5351/CKSS.2012.19.2.293
Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.