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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of the Korean Data and Information Science Society
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Korean Data and Information Science Society
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Volume & Issues
Volume 21, Issue 6 - Nov 2010
Volume 21, Issue 5 - Sep 2010
Volume 21, Issue 4 - Jul 2010
Volume 21, Issue 3 - May 2010
Volume 21, Issue 2 - Mar 2010
Volume 21, Issue 1 - Jan 2010
Selecting the target year
Estimation of nonlinear GARCH-M model
Shim, Joo-Yong ; Lee, Jang-Taek ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 831~839
Least squares support vector machine (LS-SVM) is a kernel trick gaining a lot of popularities in the regression and classification problems. We use LS-SVM to propose a iterative algorithm for a nonlinear generalized autoregressive conditional heteroscedasticity model in the mean (GARCH-M) model to estimate the mean and the conditional volatility of stock market returns. The proposed method combines a weighted LS-SVM for the mean and unweighted LS-SVM for the conditional volatility. In this paper, we show that nonlinear GARCH-M models have a higher performance than the linear GARCH model and the linear GARCH-M model via real data estimations.
Procedure for monitoring special causes and readjustment in ARMA(1,1) noise model
Lee, Jae-Heon ; Kim, Mi-Jung ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 841~852
An integrated process control (IPC) procedure is a scheme which simultaneously applies the engineering control procedure (EPC) and statistical control procedure (SPC) techniques to reduce the variation of a process. In the IPC procedure, the observed deviations are monitored during the process where adjustments are repeatedly done by its controller. Because the effects of the noise, the special cause, and the adjustment are mixed, the use and properties of the SPC procedure for the out-of-control process are complicated. This paper considers efficiency of EWMA charts for detecting special causes in an ARMA(1,1) noise model with a minimum mean squared error adjustment policy. And we propose the readjustment procedure after having a true signal. This procedure can be considered when the elimination of the special cause is not practically possible.
Anomaly detection on simulation conditions for ship-handling safety assessment
Kwon, Se-Hyug ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 853~861
Experimental conditions are set with environmental factors which can affect ship navigation. In FTS simulation, infinite simulation can be theoretically tested with no time constraint but the simulated result with the same experimental condition is repeated due to mathematical model. RTS simulation can give more resonable results but costs at lest 30 minutes for only experimental time. The mixture of two simulation methods using probability density function has been proposed: some of experimental conditions in which ship-handling is most difficult are selected with FTS and are tested in RTS. It has drawback that it does not consider the navigation route but aggregated track index. In this paper, anomaly detection approach is suggested to select some experimental conditions of FTS simulation which are most difficult in ship-handling during the navigation route. An empirical result has been shown.
Modeling sharply peaked asymmetric multi-modal circular data using wrapped Laplace mixture
Na, Jong-Hwa ; Jang, Young-Mi ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 863~871
Until now, many studies related circular data are carried out, but the focuses are mainly on mildly peaked symmetric or asymmetric cases. In this paper we studied a modeling process for sharply peaked asymmetric circular data. By using wrapped Laplace, which was firstly introduced by Jammalamadaka and Kozbowski (2003), and its mixture distributions, we considered the model fitting problem of multi-modal circular data as well as unimodal one. In particular we suggested EM algorithm to find ML estimates of the mixture of wrapped Laplace distributions. Simulation results showed that the suggested EM algorithm is very accurate and useful.
A Bayesian approach to replacement policy following the expiration of non-renewing combination warranty based on cost and downtime
Jung, Ki-Mun ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 873~882
This paper considers a Bayesian approach to replacement policy following the expiration of non-renewing combination warranty. The non-renewing combination warranty is the combination of the non-renewing free replacement warranty and the non-renewing pro-rata replacement warranty. We use the criterion based on the expected cost and the expected downtime to determine the optimal replacement period. To do so, we obtain the expected cost rate per unit time and the expected downtime per unit time, respectively. When the failure times are assumed to follow a Weibull distribution with uncertain parameters, we propose the optimal replacement policy based on the Bayesian approach. The overall value function suggested by Jiang and Ji (2002) is utilized to determine the optimal replacement period. Also, the numerical examples are presented for illustrative purpose.
Real time detection algorithm against illegal waste dumping into river based on time series intervention model
Moon, Ji-Eun ; Moon, Song-Kyu ; Kim, Tae-Yoon ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 883~890
Illegal waste dumping is one of the major problems that the government agency monitoring water quality has to face. One solution to this problem is to find an efficient way of managing and supervising the water quality under various kinds of conditions. In this article we establish WQMA (water quality monitoring algorithm) based on the time series intervention model. It turns out thatWQMA is quite successful in detecting illegal waste dumping.
Standardization for basic association measures in association rule mining
Park, Hee-Chang ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 891~899
Association rule is the technique to represent the relationship between two or more items by numerical representing for the relevance of each item in vast amounts of databases, and is most being used in data mining. The basic thresholds for association rule are support, confidence, and lift. these are used to generate the association rules. We need standardization of lift because the range of lift value is different from that of support and confidence. And also we need standardization of support and confidence to compare objectively association level of antecedent variables for one descendant variable. In this paper we propose a method for standardization of association thresholds considering marginal probability for each item to grasp objectively and exactly association level, check the conditions for association criteria and then compare association thresholds with standardized association thresholds using some concrete examples.
Geometrical description based on forward selection & backward elimination methods for regression models
Hong, Chong-Sun ; Kim, Moung-Jin ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 901~908
A geometrical description method is proposed to represent the process of the forward selection and backward elimination methods among many variable selection methods for multiple regression models. This graphical method shows the process of the forward selection and backward elimination on the first and second quadrants, respectively, of half circle with a unit radius. At each step, the SSR is represented by the norm of vector and the extra SSR or partial determinant coefficient is represented by the angle between two vectors. Some lines are dotted when the partial F test results are statistically significant, so that statistical analysis could be explored. This geometrical description can be obtained the final regression models based on the forward selection and backward elimination methods. And the goodness-of-fit for the model could be explored.
Estimating variation in the microbiological quality of seasoned soybean sprouts using probability model
Park, Jin-Pyo ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 909~916
This study aims to establish storage stability conditions for cook-chilled korean ethenic foods. In order to achieve this aims, we establish a probability model of microbial counts of cook-chilled korean side dishes product-seasoned soybean sprouts. And seasoned soybean sprouts were stored during 1 to 5 days under constant temperature conditions at 0, 5, 10 and
. Next we find confidence intervals for variation in the microbiological quality of seasoned soybean sprouts.
A study for improving data mining methods for continuous response variables
Choi, Jin-Soo ; Lee, Seok-Hyung ; Cho, Hyung-Jun ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 917~926
It is known that bagging and boosting techniques improve the performance in classification problem. A number of researchers have proved the high performance of bagging and boosting through experiments for categorical response but not for continuous response. We study whether bagging and boosting improve data mining methods for continuous responses such as linear regression, decision tree, neural network through bagging and boosting. The analysis of eight real data sets prove the high performance of bagging and boosting empirically.
Parameter estimation for exponential distribution under progressive type I interval censoring
Shin, Hye-Jung ; Lee, Kwang-Ho ; Cho, Young-Seuk ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 927~934
In this paper, we introduce a method of parameter estimation of progressive Type I interval censored sample and progressive type II censored sample. We propose a new parameter estimation method, that is converting the data which obtained by progressive type I interval censored, those data be used to estimate of the parameter in progressive type II censored sample. We used exponential distribution with unknown scale parameter, the maximum likelihood estimator of the parameter calculates from the two methods. A simulation is conducted to compare two kinds of methods, it is found that the proposed method obtains a better estimate than progressive Type I interval censoring method in terms of mean square error.
Reference priors for two parameter exponential stress-strength model
Kang, Sang-Gil ; Kim, Dal-Ho ; Le, Woo-Dong ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 935~944
In this paper, we develop the noninformative priors for the reliability in a stress-strength model where a strength X and a stress Y have independent exponential distributions with different scale parameters and a common location parameter. We derive the reference priors and prove the propriety of joint posterior distribution under the general prior including the reference priors. Through the simulation study, we show that the proposed reference priors match the target coverage probabilities in a frequentist sense.
The effect of health care reform: Testing the stability of systematic risk
Sewell, Daniel K. ; Song, Joon-Jin ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 945~950
As the U.S. Congress has continued to debate over the health care reform pushed by President Obama, there is an ample reason to believe that the systematic risk of the health care industry, especially health care plan providers, is increasing. This study measures and compares the systematic risk of two health care industry indexes and one portfolio of health care plan providers from before and after the introduction of the health care legislation into Congress in September, 2009. The Capital Asset Pricing Model (CAPM) is used to measure the systematic risk, and a dummy variable approach and the Chow test are used to formally compare the systematic risk from before and after the introduction of the legislation.
Estimation for ordered means in normal distributions
Cho, Kil-Ho ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 951~958
In this paper, we obtain the restricted maximum likelihood estimators (RMLE's) for means in normal distributions with the ordered mean constraints. The biases and mean squared errors (MSE's) of these RMLE's are approximated by Mote Carlo methods. In every case a substantial savings in MSE is obtained at the expense of a small loss in bias when using RMLE's instead of the unrestricted MLE's.
Variable selection for multiclassi cation by LS-SVM
Hwang, Hyung-Tae ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 959~965
For multiclassification, it is often the case that some variables are not important while some variables are more important than others. We propose a novel algorithm for selecting such relevant variables for multiclassification. This algorithm is base on multiclass least squares support vector machine (LS-SVM), which uses results of multiclass LS-SVM using one-vs-all method. Experimental results are then presented which indicate the performance of the proposed method.
Estimating exponentiated parameter and distribution of quotient and ratio in an exponentiated Pareto
Moon, Yeung-Gil ; Lee, Chang-Soo ; Kang, Jun-Ho ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 967~972
We shall consider estimations of an exponetiated parameter of the exponentiated Pareto distribution with known scale and threshold parameters. A quotient distribution of two independent exponentiated Pareto random variables is obtained. We also derive the distribution of the ratio of two independent exponentiated Pareto random variables.
M-quantile regression using kernel machine technique
Hwang, Chang-Ha ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 973~981
Quantile regression investigates the quantiles of the conditional distribution of a response variable given a set of covariates. M-quantile regression extends this idea by a "quantile-like" generalization of regression based on influence functions. In this paper we propose a new method of estimating M-quantile regression functions, which uses kernel machine technique. Simulation studies are presented that show the finite sample properties of the proposed M-quantile regression.
Study on the relationship between trust and organizational performance in local administrative organization- Focused on the local administrative organizations in Gangwondo-
Kim, Sun-Ok ; Park, Sung-Yong ; Lee, Hee-Choon ;
Journal of the Korean Data and Information Science Society, volume 21, issue 5, 2010, Pages 983~997
This study is to explore the relationship between trust in local administrative organization and organizational performance. Local administrative organizations provide the citizens with administrative services. Heightening the organizational performance contributes the citizens' happiness and the stream of times through organizations' change. To provide high quality of administrative service to citizens, trust in organizations is more important than any other capital. The improvement of organizational performance needs through this social capital. Factors about trust variables and organizational performance variables are extracted through the theoretical discussions. To do the research, public servants in 7 local administrative organizations of Gangwondo were asked to do the survey about how trust in organizations affects organizationa performances. The results explain that trust variables are related to organizational performance, and the local administrative organization which is high in trust is high in organizational performance. Trust in local administrative organizations improves the organizational performance internally and the organization will obtain trust from the citizens externally.