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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Communications for Statistical Applications and Methods
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The Korean Statistical Society
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Volume & Issues
Volume 17, Issue 6 - Nov 2010
Volume 17, Issue 5 - Sep 2010
Volume 17, Issue 4 - Jul 2010
Volume 17, Issue 3 - May 2010
Volume 17, Issue 2 - Mar 2010
Volume 17, Issue 1 - Jan 2010
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Hybrid Fuzzy Least Squares Support Vector Machine Regression for Crisp Input and Fuzzy Output
Shim, Joo-Yong ; Seok, Kyung-Ha ; Hwang, Chang-Ha ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 141~151
DOI : 10.5351/CKSS.2010.17.2.141
Hybrid fuzzy regression analysis is used for integrating randomness and fuzziness into a regression model. Least squares support vector machine(LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate hybrid fuzzy linear and nonlinear regression models with crisp inputs and fuzzy output using weighted fuzzy arithmetic(WFA) and LS-SVM. LS-SVM allows us to perform fuzzy nonlinear regression analysis by constructing a fuzzy linear regression function in a high dimensional feature space. The proposed method is not computationally expensive since its solution is obtained from a simple linear equation system. In particular, this method is a very attractive approach to modeling nonlinear data, and is nonparametric method in the sense that we do not have to assume the underlying model function for fuzzy nonlinear regression model with crisp inputs and fuzzy output. Experimental results are then presented which indicate the performance of this method.
Dynamic Graphics Using Line Mosaic Plot
Cha, Woon-Ock ; Lee, Kyung-Mi ; Choi, Byong-Su ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 153~164
DOI : 10.5351/CKSS.2010.17.2.153
This study is about the dynamic graphics which can be used for the exploration of the characteristics of data comprising discrete and continuous variables. Simultaneously using line mosaic plot for the relation of discrete variables and box plot together with scatter plot for the relation of continuous variables, we have applied dynamic methods among these plots to demonstrate that the structure and characteristics of the multivariate data could be easily analyzed.
Efficient Use of Auxiliary Variables in Estimating Finite Population Variance in Two-Phase Sampling
Singh, Housila P. ; Singh, Sarjinder ; Kim, Jong-Min ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 165~181
DOI : 10.5351/CKSS.2010.17.2.165
This paper presents some chain ratio-type estimators for estimating finite population variance using two auxiliary variables in two phase sampling set up. The expressions for biases and mean squared errors of the suggested c1asses of estimators are given. Asymptotic optimum estimators(AOE's) in each class are identified with their approximate mean squared error formulae. The theoretical and empirical properties of the suggested classes of estimators are investigated. In the simulation study, we took a real dataset related to pulmonary disease available on the CD with the book by Rosner, (2005).
Support Vector Quantile Regression with Weighted Quadratic Loss Function
Shim, Joo-Yong ; Hwang, Chang-Ha ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 183~191
DOI : 10.5351/CKSS.2010.17.2.183
Support vector quantile regression(SVQR) is capable of providing more complete description of the linear and nonlinear relationships among random variables. In this paper we propose an iterative reweighted least squares(IRWLS) procedure to solve the problem of SVQR with a weighted quadratic loss function. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of SVQR. Experimental results are then presented which illustrate the performance of the IRWLS procedure for SVQR.
A Comparative Study on Spatial Lattice Data Analysis - A Case Where Outlier Exists -
Kim, Su-Jung ; Choi, Seung-Bae ; Kang, Chang-Wan ; Cho, Jang-Sik ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 193~204
DOI : 10.5351/CKSS.2010.17.2.193
Recently, researchers of the various fields where the spatial analysis is needed have more interested in spatial statistics. In case of data with spatial correlation, methodologies accounting for the correlation are required and there have been developments in methods for spatial data analysis. Lattice data among spatial data is analyzed with following three procedures: (1) definition of the spatial neighborhood, (2) definition of spatial weight, and (3) the analysis using spatial models. The present paper shows a spatial statistical analysis method superior to a general statistical method in aspect estimation by using the trimmed mean squared error statistic, when we analysis the spatial lattice data that outliers are included. To show validation and usefulness of contents in this paper, we perform a small simulation study and show an empirical example with a criminal data in BusanJin-Gu, Korea.
Patent and Statistics, What's the Connection?
Jun, Sung-Hae ; Uhm, Dai-Ho ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 205~222
DOI : 10.5351/CKSS.2010.17.2.205
A patent is a right of intellectual properties to an inventor or its assignee for a limited period under an international law. Not only in an invention of new machines, but it is competitive for using and creating technology in the world based on the patents. Most of the business models are good examples for patented technology, however a statistical analyzing model could be another one. In this paper we study and analyze the patents for the statistical analyzing and data mining models which are currently applied and registered, and suggest a statistical tool for analyzing and categorizing patent data. For this study all the patents in Korea and U.S. are listed and searched to sample the only cases concerning statistics.
Asymptotics of the Variance Ratio Test for MA Unit Root Processes
Lee, Jin ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 223~229
DOI : 10.5351/CKSS.2010.17.2.223
We consider the asymptotic results of the variance ratio statistic when the underlying processes have moving average(MA) unit roots. This degenerate situation of zero spectral density near the origin cause the limit of the variance ratio to become zero. Its asymptotic behaviors are different from non-degenerating case, where the convergence rate of the variance ratio statistic is formally derived.
Comparative Study for Estimating Vaccine Efficacy in Vaccine Research under Heterogeneity
Lee, Soo-Young ; Lee, Jae-Won ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 231~239
DOI : 10.5351/CKSS.2010.17.2.231
In vaccine research, proportional hazards model including only first event have been widely used for estimating vaccine efficacy because it is easy to interpret and convenient. However, this method causes not only loss of information but also biased result when heterogeneity of study subject in exposure and susceptibility exists. Furthermore, it is hard to ignore the possibility that each event is correlated with each other in the repeated events. Therefore, we compare various statistical models to estimate vaccine efficacy under various situations with heterogeneity and event dependency.
Transmission Effect of Price Variations
Kim, Tae-Ho ; Ann, Ji-Hee ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 241~253
DOI : 10.5351/CKSS.2010.17.2.241
As standard unit root tests are empirically proved to fail to reject the null hypothesis of a unit root for many economic and business time series, it is doubtful that most of those series are informative about the existence of a unit root or that those tests are powerful against relevant alternative hypotheses. This study attempts to perform tests of the null hypothesis of stationarity as well as tests of the null hypothesis of a unit root using the time series data of housing prices in the major metropolitan areas. The results of the additional analyses such as lead-lag, cross-correlation and impulse response for testing the statistical interrelationships between the prices are generally found to be consistent.
Cumulative Impulse Response Functions for a Class of Threshold-Asymmetric GARCH Processes
Park, J.A. ; Baek, J.S. ; Hwang, S.Y. ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 255~261
DOI : 10.5351/CKSS.2010.17.2.255
A class of threshold-asymmetric GRACH(TGARCH, hereafter) models has been useful for explaining asymmetric volatilities in the field of financial time series. The cumulative impulse response function of a conditionally heteroscedastic time series often measures a degree of unstability in volatilities. In this article, a general form of the cumulative impulse response function of the TGARCH model is discussed. In particular, We present formula in their closed forms for the first two lower order models, viz., TGARCH(1, 1) and TGARCH(2, 2).
The Comparison of Imputation Methods in Space Time Series Data with Missing Values
Lee, Sung-Duck ; Kim, Duck-Ki ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 263~273
DOI : 10.5351/CKSS.2010.17.2.263
Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood or as random variables and predicted by the conditional expectation of the unknown values given the data. The purpose of this study is to impute missing values which are regarded as the maximum likelihood estimator and random variable in incomplete data and to compare with two methods using ARMA and STAR model. For illustration, the Mumps data reported from the national capital region monthly over the years 2001~2009 are used, and estimate precision of missing values and forecast precision of future data are compared with two methods.
A Comparison of the Interval Estimations for the Difference in Paired Areas under the ROC Curves
Kim, Hee-Young ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 275~292
DOI : 10.5351/CKSS.2010.17.2.275
Receiver operating characteristic(ROC) curves can be used to assess the accuracy of tests measured on ordinal or continuous scales. The most commonly used measure for the overall diagnostic accuracy of diagnostic tests is the area under the ROC curve(AUC). When two ROC curves are constructed based on two tests performed on the same individuals, statistical analysis on differences between AUCs must take into account the correlated nature of the data. This article focuses on confidence interval estimation of the difference between paired AUCs. We compare nonparametric, maximum likelihood, bootstrap and generalized pivotal quantity methods, and conduct a monte carlo simulation to investigate the probability coverage and expected length of the four methods.
Forecasting KOSPI 200 Volatility by Volatility Measurements
Choi, Young-Soo ; Lee, Hyun-Jung ;
Communications for Statistical Applications and Methods, volume 17, issue 2, 2010, Pages 293~308
DOI : 10.5351/CKSS.2010.17.2.293
In this paper, we examine the forecasting KOSPI 200 realized volatility by volatility measurements. The empirical investigation for KOSPI 200 daily returns is done during the period from 3 January 2003 to 29 June 2007. Since Korea Exchange(KRX) will launch VKOSPI futures contract in 2010, forecasting VKOSPI can be an important issue. So we analyze which volatility measurements forecast VKOSPI better. To test this hypothesis, we use 5-minute interval returns to measure realized volatilities. Also, we propose a new methodology that reflects the synchronized bidding and simultaneously takes it account the difference between overnight volatility and intra-daily volatility. The t-test and F-test show that our new realized volatility is not only different from the realized volatility by a conventional method at less than 0.01% significance level, also more stable in summary statistics. We use the correlation analysis, regression analysis, cross validation test to investigate the forecast performance. The empirical result shows that the realized volatility we propose is better than other volatilities, including historical volatility, implied volatility, and convention realized volatility, for forecasting VKOSPI. Also, the regression analysis on the predictive abilities for realized volatility, which is measured by our new methodology and conventional one, shows that VKOSPI is an efficient estimator compared to historical volatility and CRR implied volatility.