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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
Communications for Statistical Applications and Methods
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Journal DOI :
The Korean Statistical Society
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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
A Note on Deconvolution Estimators when Measurement Errors are Normal
Lee, Sung-Ho ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 517~526
DOI : 10.5351/CKSS.2012.19.4.517
In this paper a support vector method is proposed for use when the sample observations are contaminated by a normally distributed measurement error. The performance of deconvolution density estimators based on the support vector method is explored and compared with kernel density estimators by means of a simulation study. An interesting result was that for the estimation of kurtotic density, the support vector deconvolution estimator with a Gaussian kernel showed a better performance than the classical deconvolution kernel estimator.
Analysis of the Effect of Wind Direction on Ozone Level
Na, Jong-Hwa ; Sung, Su-Jin ; Yu, Hye-Kyung ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 527~536
DOI : 10.5351/CKSS.2012.19.4.527
In this paper we analyze the effect of circular variables such as wind direction, time and month on the ozone level. In particular, we examined the effect of wind direction by exploratory data analysis methods and provide the correlation and regression analyzes in the cases including all circular explanatory variables. In the analysis, we convert time and month variables to circular variables and analyze the effect of these variables on regression analysis; in addition, we also consider circular-circular regression. We used weather condition and air pollution data collected from Dongdaemoon district of Seoul in 2007.
A Study on the Power Comparison between Logistic Regression and Offset Poisson Regression for Binary Data
Kim, Dae-Youb ; Park, Heung-Sun ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 537~546
DOI : 10.5351/CKSS.2012.19.4.537
In this paper, for analyzing binary data, Poisson regression with offset and logistic regression are compared with respect to the power via simulations. Poisson distribution can be used as an approximation of binomial distribution when n is large and p is small; however, we investigate if the same conditions can be held for the power of significant tests between logistic regression and offset poisson regression. The result is that when offset size is large for rare events offset poisson regression has a similar power to logistic regression, but it has an acceptable power even with a moderate prevalence rate. However, with a small offset size (< 10), offset poisson regression should be used with caution for rare events or common events. These results would be good guidelines for users who want to use offset poisson regression models for binary data.
Social Network Analysis and Its Applications for Authors and Keywords in the JKSS
Kim, Jong-Goen ; Choi, Soon-Kuek ; Choi, Yong-Seok ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 547~558
DOI : 10.5351/CKSS.2012.19.4.547
Social network analysis is a graphical technique to search the relationships and characteristics of nodes (people, companies, and organizations) and an important node for positioning a visualized social network figure; however, it is difficult to characterize nodes in a social network figure. Therefore, their relationships and characteristics could be presented through an application of correspondence analysis to an affiliation matrix that is a type of similarity matrix between nodes. In this study, we provide the relationships and characteristics around authors and keywords in the JKSS(Journal of the Korean Statistical Society) of the Korean Statistical Society through the use of social network analysis and correspondence analysis.
Comparison of Methods for Reducing the Dimension of Compositional Data with Zero Values
Song, Taeg-Youn ; Choi, Byung-Jin ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 559~569
DOI : 10.5351/CKSS.2012.19.4.559
Compositional data consist of compositions that are non-negative vectors of proportions with the unit-sum constraint. In disciplines such as petrology and archaeometry, it is fundamental to statistically analyze this type of data. Aitchison (1983) introduced a log-contrast principal component analysis that involves logratio transformed data, as a dimension-reduction technique to understand and interpret the structure of compositional data. However, the analysis is not usable when zero values are present in the data. In this paper, we introduce 4 possible methods to reduce the dimension of compositional data with zero values. Two real data sets are analyzed using the methods and the obtained results are compared.
Software Taskset Processing Evaluation Based on a Mixed Debugging Process
Kim, U-Jung ; Lee, Chong-Hyung ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 571~577
DOI : 10.5351/CKSS.2012.19.4.571
Modules that consist of software are respectively coded in the early development phase and the modules are unified as a software. After unification, the software is repeatedly tested with a given taskset (the set of module tasks that are tested simultaneously) until a required performance level is satisfied. In this paper, we expand the one-module software debugging model of Jang and Lee (2011) to a multi-module debugging model and derive the taskset completion probability and the mean of the completed tasksets under the assumption that the processing times of module tasks given in a taskset are mutually dependent.
Bootstrap Bandwidth Selection Methods for Local Linear Jump Detector
Park, Dong-Ryeon ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 579~590
DOI : 10.5351/CKSS.2012.19.4.579
Local linear jump detection in a discontinuous regression function involves the choice of the bandwidth and the performance of a local linear jump detector depends heavily on the choice of the bandwidth. However, little attention has been paid to this important issue. In this paper we propose two fully data adaptive bandwidth selection methods for a local linear jump detector. The performance of the proposed methods are investigated through a simulation study.
Skew Normal Boxplot and Outliers
Huh, Myung-Hoe ; Lee, Yong-Goo ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 591~595
DOI : 10.5351/CKSS.2012.19.4.591
We frequently use Tukey's boxplot to identify outliers in the batch of observations of the continuous variable. In doing so, we implicitly assume that the underlying distribution belongs to the family of normal distributions. Such a practice of data handling is often superficial and improper, since in reality too many variables manifest the skewness. In this short paper, we build a modified boxplot and set the outlier identification procedure by assuming that the observations are generated from the skew normal distribution (Azzalini, 1985), which is an extension of the normal distribution. Statistical performance of the proposed procedure is examined with simulated datasets.
Choosing the Tuning Constant by Laplace Approximation
Ahn, Sung-Mahn ; Kwon, Suhn-Beom ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 597~605
DOI : 10.5351/CKSS.2012.19.4.597
Evidence framework enables us to determine the tuning constant in a penalized likelihood formula. We apply the framework to the estimating parameters of normal mixtures. Evidence, which is a solely data-dependent measure, can be evaluated by Laplace approximation. According to a synthetic data simulation, we found that the proper values of the tuning constant can be systematically obtained.
A Goodness-of-Fit Test for Multivariate Normal Distribution Using Modified Squared Distance
Yim, Mi-Hong ; Park, Hyun-Jung ; Kim, Joo-Han ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 607~617
DOI : 10.5351/CKSS.2012.19.4.607
The goodness-of-fit test for multivariate normal distribution is important because most multivariate statistical methods are based on the assumption of multivariate normality. We propose goodness-of-fit test statistics for multivariate normality based on the modified squared distance. The empirical percentage points of the null distribution of the proposed statistics are presented via numerical simulations. We compare performance of several test statistics through a Monte Carlo simulation.
Adaptive Noise Reduction Algorithm for an Image Based on a Bayesian Method
Kim, Yeong-Hwa ; Nam, Ji-Ho ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 619~628
DOI : 10.5351/CKSS.2012.19.4.619
Noise reduction is an important issue in the field of image processing because image noise lowers the quality of the original pure image. The basic difficulty is that the noise and the signal are not easily distinguished. Simple smoothing is the most basic and important procedure to effectively remove the noise; however, the weakness is that the feature area is simultaneously blurred. In this research, we use ways to measure the degree of noise with respect to the degree of image features and propose a Bayesian noise reduction method based on MAP (maximum a posteriori). Simulation results show that the proposed adaptive noise reduction algorithm using Bayesian MAP provides good performance regardless of the level of noise variance.
Bivariate ROC Curve and Optimal Classification Function
Hong, C.S. ; Jeong, J.A. ;
Communications for Statistical Applications and Methods, volume 19, issue 4, 2012, Pages 629~638
DOI : 10.5351/CKSS.2012.19.4.629
We propose some methods to obtain optimal thresholds and classification functions by using various cutoff criterion based on the bivariate ROC curve that represents bivariate cumulative distribution functions. The false positive rate and false negative rate are calculated with these classification functions for bivariate normal distributions.