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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 22, Issue 6 - Dec 2011
Volume 22, Issue 5 - Oct 2011
Volume 22, Issue 4 - Jul 2011
Volume 22, Issue 3 - May 2011
Volume 22, Issue 2 - Mar 2011
Volume 22, Issue 1 - Jan 2011
Selecting the target year
Analysis of Korean GDP by unobserved components model
Seong, Byeong-Chan ; Lee, Seung-Kyung ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 829~837
Since Harvey (1989), many approaches for applying unobserved components (UC) models to both univariate and multivariate time series analysis have been developed. However, practitioners still tend to use traditional methods such as exponential smoothing or ARIMA models for modeling and predicting time series data. It is well known that the UC model combines the flexibility of ARIMA models and the easy interpretability of exponential smoothing models by using unobserved components such as trend, cycle, season, and irregular components. This study reviews the UC model and compares its relative performances with those of the other models in modeling and predicting the real gross domestic products (GDP) in Korea. We conclude that the optimal model is the UC model on basis of root mean squared error.
Autocovariance based estimation in the linear regression model
Park, Cheol-Yong ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 839~847
In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.
Determinants of job finding using student's characteristic information
Cho, Jang-Sik ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 849~856
In this paper, we study the influence analysis of admission and enrollment variables including individual characteristics variables on employment of graduate students at K university. First, logistic regression analysis is used to examine the main effects of admission, enrollment variables including student's individual characteristics on employment. Also, decision tree analysis is used to examine the interaction effects for the variables on employment. The results of this paper may be helpful to K university in designing effective job finding strategies for graduate students.
Curriculum development for education and training of admissions officer - J university case
Han, Dong-Wook ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 857~866
The role of admissions officers is to evaluate the overall qualification of applicants, which consists of various qualitative and subjective metrics The core requirement of admissions officers is not only assessment of students' qualification based on the application but also verification of the data stem from the various sources. The issue for fairness on selection is the crucial agenda for the whole process of university entrance. The educational program aimed on the enhancement the expertise of admissions officers is required to guarantee the fair selection of whole evaluation process. In this paper we developed educational training program for admissions officers of J university. The 9 core dimensions of curriculum is presented to train admissions officers and the stepwise requirements of achieving each subject's goal are defined.
Study on after-school programs of disability schools
Nam, Mi-Ja ; Cho, Kil-Ho ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 867~875
In this paper, we investigate the true state of administration about after-school programs of disability schools, study the requests of the children' protectors, and support the basic materials to find the systematic and concrete after-school programs of disability schools.
A study on removal of unnecessary input variables using multiple external association rule
Cho, Kwang-Hyun ; Park, Hee-Chang ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 877~884
The decision tree is a representative algorithm of data mining and used in many domains such as retail target marketing, fraud detection, data reduction, variable screening, category merging, etc. This method is most useful in classification problems, and to make predictions for a target group after dividing it into several small groups. When we create a model of decision tree with a large number of input variables, we suffer difficulties in exploration and analysis of the model because of complex trees. And we can often find some association exist between input variables by external variables despite of no intrinsic association. In this paper, we study on the removal method of unnecessary input variables using multiple external association rules. And then we apply the removal method to actual data for its efficiencies.
The benefit analysis of constructing the visual conservation center
Jeong, Ki-Ho ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 885~893
Visual conservation center is a facility for the preservation of visual materials such as film and digital image data, and its construction project is currently being considered. This study evaluates the economic benefit of the project using CVM (Contingent Valuation Method), the main method of nonmarket evaluation in the both domestic and foreign literature. Survey data used in the analysis was collected in seven major metropolitan cities using person-to-person interviews and a logit model is used for the econometric estimation. The economic benefit measured by a household's average willing to pay (WTP) per year is shown to amount to 8,958 won.
Confidence interval forecast of exchange rate based on bootstrap method during economic crisis
Kim, Tae-Yoon ; Kwon, O-Jin ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 895~902
This paper is mainly concerned about providing confidence prediction interval for exchange rate during economic crisis. Our proposed method is to use block bootstrap method for prediction interval for next day. It is shown that block bootstrap method is particularly effective for interval prediction of exchange rate during economic crisis.
A seasonal growth curve estimation for continuous spawning fishes
Choi, Il-Su ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 903~910
The von Bertalanffy growth function (VBGF) is the result of the antagonistic effects of anabolism and cataboliem. However VGBF has limitations for describing the growth of continuous spawning fishes. In the present work, a new equation is proposed where the growth parameter Kis substituted by a function related to the sea surface temperature of spawning period. Examples for natural population of Pacific Anchovy are presented.
The effect investigation of the delirium by Bayesian network and radial graph
Lee, Jea-Young ; Bae, Jae-Young ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 911~919
In recent medical analysis, it becomes more important to looking for risk factors related to mental illness. If we find and identify their relevant characteristics of the risk factors, the disease can be prevented in advance. Moreover, the study can be helpful to medical development. These kinds of studies of risk factors for mental illness have mainly been discussed by using the logistic regression model. However in this paper, data mining techniques such as CART, C5.0, logistic, neural networks and Bayesian network were used to search for the risk factors. The Bayesian network of the above data mining methods was selected as most optimal model by applying delirium data. Then, Bayesian network analysis was used to find risk factors and the relationship between the risk factors are identified through a radial graph.
An analysis of satisfaction index on computer education of university using kernel machine
Pi, Su-Young ; Park, Hye-Jung ; Ryu, Kyung-Hyun ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 921~929
In Information age, the academic liberal art Computer education course set up goals for promoting computer literacy and for developing the ability to cope actively with in Information Society and for improving productivity and competition among nations. In this paper, we analyze on discovering of decisive property and satisfaction index to have a influence on computer education on university students. As a preprocessing method, the proposed method select optimum property using correlation feature selection of machine learning tool based on Java and then we use multiclass least square support vector machine based on statistical learning theory. After applying that compare with multiclass support vector machine and multiclass least square support vector machine, we can see the fact that the proposed method have a excellent result like multiclass support vector machine in analysis of the academic liberal art computer education satisfaction index data.
A new classification method using penalized partial least squares
Kim, Yun-Dae ; Jun, Chi-Hyuck ; Lee, Hye-Seon ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 931~940
Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and PCA based discriminant analysis by some real and artificial data. It is concluded that the new method has better power as compared with other methods.
Clustering analysis of Korea's meteorological data
Yeo, In-Kwon ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 941~949
In this paper, 72 weather stations in Korea are clustered by the hierarchical agglomerative procedure based on the average linkage method. We compare our clusters and stations divided by mountain chains which are applied to study on the impact analysis of foodborne disease outbreak due to climate change.
Variable selection in L1 penalized censored regression
Hwang, Chang-Ha ; Kim, Mal-Suk ; Shi, Joo-Yong ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 951~959
The proposed method is based on a penalized censored regression model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log likelihood function of censored regression model. It provide the efficient computation of regression parameters including variable selection and leads to the generalized cross validation function for the model selection. Numerical results are then presented to indicate the performance of the proposed method.
Circular regression using geodesic lines
Kim, Sung-su ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 961~966
Circular variables are those that have a period in its range. Their examples include direction of animal migration, and time of drug administration, just to mention a few. Statistical analysis of circular variables is quite different from that of linear variable due to its periodic nature. In this paper, the author proposes new circular regression models using geodesic lines on the surface of the sample space of the response and the predictor variables.
A note on Box-Cox transformation and application in microarray data
Rahman, Mezbahur ; Lee, Nam-Yong ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 967~976
The Box-Cox transformation is a well known family of power transformations that brings a set of data into agreement with the normality assumption of the residuals and hence the response variable of a postulated model in regression analysis. Normalization (studentization) of the regressors is a common practice in analyzing microarray data. Here, we implement Box-Cox transformation in normalizing regressors in microarray data. Pridictabilty of the model can be improved using data transformation compared to studentization.
Bayesian estimation in the generalized half logistic distribution under progressively type-II censoring
Kim, Yong-Ku ; Kang, Suk-Bok ; Se, Jung-In ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 977~989
The half logistic distribution has been used intensively in reliability and survival analysis especially when the data is censored. In this paper, we provide Bayesian estimation of the shape parameter and reliability function in the generalized half logistic distribution based on progressively Type-II censored data under various loss functions. We here consider conjugate prior and noninformative prior and corresponding posterior distributions are obtained. As an illustration, we examine the validity of our estimation using real data and simulated data.
Design and evaluation of a GQS-based time-critical event dissemination for distributed clouds
Bae, Ihn-Han ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 989~998
Cloud computing provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location and configuration of the system that delivers the services. Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world. One of the fundamental challenges in geographically distributed clouds is to provide efficient algorithms for supporting inter-cloud data management and dissemination. In this paper, we propose a group quorum system (GQS)-based dissemination for improving the interoperability of inter-cloud in time-critical event dissemination service, such as computing policy updating, message sharing, event notification and so forth. The proposed GQS-based method organizes these distributed clouds into a group quorum ring overlay to support a constant event dissemination latency. Our numerical results show that the GQS-based method improves the efficiency as compared with Chord-based and Plume methods.
Asymmetric least squares regression estimation using weighted least squares support vector machine
Hwan, Chang-Ha ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 999~1005
This paper proposes a weighted least squares support vector machine for asymmetric least squares regression. This method achieves nonlinear prediction power, while making no assumption on the underlying probability distributions. The cross validation function is introduced to choose optimal hyperparameters in the procedure. Experimental results are then presented which indicate the performance of the proposed model.
Default Bayesian testing for the bivariate normal correlation coefficient
Kang, Sang-Gil ; Kim, Dal-Ho ; Lee, Woo-Dong ;
Journal of the Korean Data and Information Science Society, volume 22, issue 5, 2011, Pages 1007~1016
This article deals with the problem of testing for the correlation coefficient in the bivariate normal distribution. We propose Bayesian hypothesis testing procedures for the bivariate normal correlation coefficient under the noninformative prior. The noninformative priors are usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the default Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. A simulation study and an example are provided.