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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Korean Journal of Applied Statistics
Journal Basic Information
Journal DOI :
The Korean Statistical Society
Editor in Chief :
Volume & Issues
Volume 26, Issue 6 - Dec 2013
Volume 26, Issue 5 - Oct 2013
Volume 26, Issue 4 - Aug 2013
Volume 26, Issue 3 - Jun 2013
Volume 26, Issue 2 - Apr 2013
Volume 26, Issue 1 - Feb 2013
Selecting the target year
A Study on the Stratified Cluster Replicated Systematic Unrelated Question Model
Lee, Gi-Sung ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 209~222
DOI : 10.5351/KJAS.2013.26.2.209
We apply stratified cluster sampling to a replicated systematic unrelated question model for a large scale survey in which the population is comprised of several strata developed by several clusters and with sensitive parameters. We first present a replicated systematic unrelated question model using an unrelated question model to procure sensitive information from the population of clusters and then develop a suggested model to an unrelated question by a stratified cluster replicated systematic sampling that can be used in large population of strata. We cover the proportional and optimum allocation for the suggested model. Finally, we compare and analyze the efficiency of the suggested model with the replicated systematic unrelated question model.
Implementation of Mahalanobis-Taguchi System for the Election of Major League Baseball Hitters to the Hall of Fame
Kim, Su Whan ; Park, Changsoon ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 223~236
DOI : 10.5351/KJAS.2013.26.2.223
Various statistical classification methods to predict election to the Major League Baseball hall of fame of are implemented and their accuracies are compared. Seventeen independent variables are selected from the data of candidates eligible for the hall of fame and well-known classification methods such as discriminant analysis and logistic regression as well as the recently proposed Mahalanobis-Taguchi system(MTS). The MTS showed a better performance than the others in classification accuracy because it is especially efficient in cases where multivariate data does not constitute directionally geographical groups according to attributes.
Estimating Automobile Insurance Premiums Based on Time Series Regression
Kim, Yeong-Hwa ; Park, Wonseo ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 237~252
DOI : 10.5351/KJAS.2013.26.2.237
An estimation model for premiums and components is essential to determine reasonable insurance premiums. In this study, we introduce diverse models for the estimation of property damage premiums(premium, depth and frequency) that include a regression model using a dummy variable, additive independent variable model, autoregressive error model, seasonal ARIMA model and intervention model. In addition, the actual property damage premium data was used to estimate the premium, depth and frequency for each model. The estimation results of the models are comparatively examined by comparing the RMSE(Root Mean Squared Errors) of estimates and actual data. Based on real data analysis, we found that the autoregressive error model showed the best performance.
Information Variables for the Predictability of Future Changes in Real Growth
Kim, Tae Ho ; Jung, Jae Hwa ; Kim, Min Jeong ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 253~265
DOI : 10.5351/KJAS.2013.26.2.253
It has been interested in developing useful information variables that are able to predict the future movement of final objects to attain the specific policy and strategic target. Term structure of interest rates is known as an important variable to predict future business and economic activity, yet there is little empirical work on the predictability of future changes in real output. This study attempts to develop the statistical model and examine whether domestic term structure of interest rates can predict variations of future cumulative changes in real growth on a long time horizon.
Effects of Financial College Tuition Support by Korean Parents using a Hierarchical Bayes Model
Oh, Man-Suk ; Oh, Hyun Sook ; Oh, Min Jung ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 267~280
DOI : 10.5351/KJAS.2013.26.2.267
College tuition is a significant economic, social, and political issue in Korea. We conduct a Bayesian analysis of a hierarchical model to address the factors related to college tuition based on a survey data collected by Statistics Korea. A binary response variable is selected depending on if more than 70% of tuition costs are supported by parents, and a hierarchical Probit model is constructed with areas as groups. A set of explanatory variables is selected from a factor analysis of available variables in the survey. A Markov chain Monte Carlo algorithm is used to estimate parameters. From the analysis results, income and stress are significantly related to college tuition support from parents. Parents with high income tend to support children's college tuition and students with parents' financial support tend to be mentally less stressed; subsequently, this shows that the economic status of parents significantly affects the mental health of college students. Gender, a healthy life style, and college satisfaction are not significant factors. Comparing areas in terms of the degrees of correlation between stress/income and tuition support from parents, students in Kangwon-do are the most mentally stressed when parents' support is limited; in addition, the positive correlation between parents support and income is stronger in big cities compared to provincial areas.
DD-plot for Detecting the Out-of-Control State in Multivariate Process
Jang, Dae-Heung ; Yi, Seongbaek ; Kim, Youngil ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 281~290
DOI : 10.5351/KJAS.2013.26.2.281
It is well known that the DD-plot is a useful graphical tool for non-parametric classification. In this paper, we propose another use of DD-plot for detecting the out-of-control state in multivariate process. We suggested a dynamic version of DD-plot and its accompanying a quality index plot in such case.
Nonparametric Detection Methods against DDoS Attack
Lee, J.L. ; Hong, C.S. ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 291~305
DOI : 10.5351/KJAS.2013.26.2.291
Collective traffic data (BPS, PPS etc.) for detection against the distributed denial of service attack on network is the time sequencing big data. The algorithm to detect the change point in the big data should be accurate and exceed in detection time and detection capability. In this work, the sliding window and discretization method is used to detect the change point in the big data, and propose five nonparametric test statistics using empirical distribution functions and ranks. With various distribution functions and their parameters, the detection time and capability including the detection delay time and the detection ratio for five test methods are explored and discussed via monte carlo simulation and illustrative examples.
Cross Platform Data Analysis in Microarray Experiment
Lee, Jangmee ; Lee, Sunho ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 307~319
DOI : 10.5351/KJAS.2013.26.2.307
With the rapid accumulation of microarray data, it is a significant challenge to integrate available data sets addressing the same biological questions that can provide more samples and better experimental results. Sometimes, different microarray platforms make it difficult to effectively integrate data from several studies and there is no consensus on which method is the best to produce a single and unified data set. Methods using median rank score, quantile discretization and standardization (which directly combine rescaled gene expression values) and meta-analysis (which combine the results of individual studies at the interpretative level) are reviewed. Real data examples downloaded from GEO are used to compare the performance of these methods and to evaluate if the combined data set detects more reliable information from the separated data sets or not.
Suggestions of Partial Credibilities for Proper Non-Life Insurance Premium
Kim, Myung Joon ; Choi, Jung-Ah ; Kim, Yeong-Hwa ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 321~333
DOI : 10.5351/KJAS.2013.26.2.321
Credibility theory is one of important theories in actuarial science to produce proper insurance premium. In this paper, new partial credibilities are proposed and introduced with widely accepted credibility theories such as rule of relative exposure volume, square root rule, B
hlmann credibility and B
hlmann-Straub credibility. Also, with credibilities estimated by current and newly suggested, the performance of the accuracy for estimating the risk is compared through real data analysis and we show that the newly suggested methods are improving the performance by reducing the error.
Empirical Mode Decomposition using the Second Derivative
Park, Min-Su ; Kim, Donghoh ; Oh, Hee-Seok ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 335~347
DOI : 10.5351/KJAS.2013.26.2.335
There are various types of real world signals. For example, an electrocardiogram(ECG) represents myocardium activities (contraction and relaxation) according to the beating of the heart. ECG can be expressed as the fluctuation of ampere ratings over time. A signal is a composite of various types of signals. An orchestra (which boasts a beautiful melody) consists of a variety of instruments with a unique frequency; subsequently, each sound is combined to form a perfect harmony. Various research on how to to decompose mixed stationary signals have been conducted. In the case of non-stationary signals, there is a limitation to use methodologies for stationary signals. Huang et al. (1998) proposed empirical mode decomposition(EMD) to deal with non-stationarity. EMD provides a data-driven approach to decompose a signal into intrinsic mode functions according to local oscillation through the identification of local extrema. However, due to the repeating process in the construction of envelopes, EMD algorithm is not efficient and not robust to a noise, and its computational complexity tends to increase as the size of a signal grows. In this research, we propose a new method to extract a local oscillation embedded in a signal by utilizing the second derivative.
Daily Peak Load Forecasting for Electricity Demand by Time series Models
Lee, Jeong-Soon ; Sohn, H.G. ; Kim, S. ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 349~360
DOI : 10.5351/KJAS.2013.26.2.349
Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.
The Similarity Plot for Comparing Clustering Methods
Jang, Dae-Heung ;
Korean Journal of Applied Statistics, volume 26, issue 2, 2013, Pages 361~373
DOI : 10.5351/KJAS.2013.26.2.361
There are a wide variety of clustering algorithms; subsequently, we need a measure of similarity between two clustering methods. Such a measure can compare how well different clustering algorithms perform on a set of data. More numbers of compared clustering algorithms allow for more number of valuers for a measure of similarity between two clustering methods. Thus, we need a simple tool that presents the many values of a measure of similarity to compare many clustering methods. We suggest some graphical tools to compareg many clustering methods.