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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Korean Journal of Applied Statistics
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Journal DOI :
The Korean Statistical Society
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Volume & Issues
Volume 19, Issue 3 - Nov 2006
Volume 19, Issue 2 - Jul 2006
Volume 19, Issue 1 - Mar 2006
Selecting the target year
Traffic Summary for Analyzing Network Load in Mobile Communication System
Lee, Y.D. ; Koh, S.G. ; Ahn, B.J. ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 379~393
DOI : 10.5351/KJAS.2006.19.3.379
In this paper, we propose a method to summarize the monthly traffic amount for analyzing network load in mobile communication system. We used the traffic data obtained from a domestic telecommunication company. Based on the statistical properties of the traffic data, we devise an efficient method to summarize monthly traffic amount.
Modified Product-Limit Estimator via Period Analysis
Kim, Jin-Heum ; Ahn, Yoon-Ok ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 395~406
DOI : 10.5351/KJAS.2006.19.3.395
Long-term survival rates are the most commonly used outcome measures for patients with cancer. However, traditional long-term survival statistics, which are derived by cohort analysis or complete analysis, essentially reflect the survival expectations of patients diagnosed many years ago. They are often outdated at the time they become available. In this article, we propose a modified product-limit method to obtain up-to-date estimates of long-term survival rates via a period analysis. The proposed method is illustrated with cancer registry data collected from January 1993 to December 1997.
Analysis of Household Overdue Loans by Using a Two-stage Generalized Linear Model
Oh, Man-Suk ; Oh, Hyeon-Tak ; Lee, Young-Mi ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 407~419
DOI : 10.5351/KJAS.2006.19.3.407
In this paper, we analyze household overdue loans in Korea which has been causing serious social and economical problems. We consider customers of Bank A in Korea and focus on overdue cash services which have been snowballing in the past few years. From analysis of overdue loans, one can predict possible delays for current customers as well as build a credit evaluation and risk management system for future customers. As a statistical analytical tool, we propose a two-stage Generalized Linear regression Model (GLM) which assumes a logistic model for presence/non-presence of overdue and a gamma model for the amount of overdue in the case of overdue. We perform goodness of fit test for the two-stage model and select significant explanatory variables in each stage of the model. It turns out that age, the amount of credit loans from other financial companies, the amount of cash service from other companies, debit balance, the average amount of cash service, and net profit are important explanatory variables relevant to overdue credit card cash service in Korea.
Small Area Estimation Using Bayesian Auto Poisson Model with Spatial Statistics
Lee, Sang-Eun ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 421~430
DOI : 10.5351/KJAS.2006.19.3.421
In sample survey sample designs are performed by geographically-based domain such as countries, states and metropolitan areas. However mostly statistics of interests are smaller domain than sample designed domain. Then sample sizes are typically small or even zero within the domain of interest. Shin and Lee(2003) mentioned Spatial Autoregressive(SAR) model in small area estimation model-based method and show the effectiveness by MSE. In this study, Bayesian Auto-Poisson Model is applied in model-based small area estimation method and compare the results with SAR model using MSE ME and bias check diagnosis using regression line. In this paper Survey of Disability, Aging and Cares(SDAC) data are used for simulation studies.
Functional Data Analysis of Temperature and Precipitation Data
Kang, Kee-Hoon ; Ahn, Hong-Se ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 431~445
DOI : 10.5351/KJAS.2006.19.3.431
In this paper we review some methods for analyzing functional data and illustrate real application of functional data analysis. Representing methods for functional data by using basis function, analyzing functional variation by functional principal component analysis and functional linear models are reviewed. For a real application, we use temperature and precipitation data measured in Korea from the January of 1970 to the May of 2004. We apply functional principal component analysis for each data and test the significance of regional division done by using shining hours. We also estimate functional regression model for temperature and precipitation.
On Multiple Comparison of Geometric Means of Exponential Parameters via Graphical Model
Kim, Dae-Hwang ; Kim, Hea-Jung ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 447~460
DOI : 10.5351/KJAS.2006.19.3.447
This paper develops a multiple comparison method for finding an optimal ordering of K geometric means of exponential parameters. This is based on the paired comparison experimental arrangement whose results can naturally be represented by a completely oriented graph. Introducing posterior preference probabilities and stochastic transitivity conditions to the graph, we obtain a new graphical model that yields criteria for the optimal ordering in the multiple comparison. Necessary theories involved in the method and some computational aspects are provided. Some numerical examples are given to illustrate the efficiency of the suggested method.
Test of Model Specification in Panel Regression Model with Two Error Components
Song, Seuck-Heun ; Kim, Young-Ji ; Hwang, Sun-Young ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 461~479
DOI : 10.5351/KJAS.2006.19.3.461
This paper derives joint and conditional Lagrange multiplier tests based on Double-Length Artificial Regression(DLR) for testing functional form and/or the presence of individual(time) effect in a panel regression model. Small sample properties of these tests are assessed by Monte Carlo study, and comparisons are made with LM tests based on Outer Product Gradient(OPG). The results show that the proposed DLR based LM tests have the most appropriate finite sample performance.
Estimation and Performance Analysis of Risk Measures using Copula and Extreme Value Theory
Yeo, Sung-Chil ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 481~504
DOI : 10.5351/KJAS.2006.19.3.481
VaR, a tail-related risk measure is now widely used as a tool for a measurement and a management of financial risks. For more accurate measurement of VaR, recently we are particularly concerned about the approach based on extreme value theory rather than the traditional method based on the assumption of normal distribution. However, many studies about the approaches using extreme value theory was done only for the univariate case. In this paper, we discuss portfolio risk measurements with modelling multivariate extreme value distributions by combining copulas and extreme value theory. We also discuss the estimation of ES together with VaR as portfolio risk measures. Finally, we investigate the relative superiority of EVT-copula approach than variance-covariance method through the back-testing of an empirical data.
Bayesian Analysis of a Zero-inflated Poisson Regression Model: An Application to Korean Oral Hygienic Data
Lim, Ah-Kyoung ; Oh, Man-Suk ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 505~519
DOI : 10.5351/KJAS.2006.19.3.505
We consider zero-inflated count data, which is discrete count data but has too many zeroes compared to the Poisson distribution. Zero-inflated data can be found in various areas. Despite its increasing importance in practice, appropriate statistical inference on zero-inflated data is limited. Classical inference based on a large number theory does not fit unless the sample size is very large. And regular Poisson model shows lack of St due to many zeroes. To handle the difficulties, a mixture of distributions are considered for the zero-inflated data. Specifically, a mixture of a point mass at zero and a Poisson distribution is employed for the data. In addition, when there exist meaningful covariates selected to the response variable, loglinear link is used between the mean of the response and the covariates in the Poisson distribution part. We propose a Bayesian inference for the zero-inflated Poisson regression model by using a Markov Chain Monte Carlo method. We applied the proposed method to a Korean oral hygienic data and compared the inference results with other models. We found that the proposed method is superior in that it gives small parameter estimation error and more accurate predictions.
A Study of Generalized Maximum Entropy Estimator for the Panel Regression Model
Song, Seuck-Heun ; Cheon, Soo-Young ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 521~534
DOI : 10.5351/KJAS.2006.19.3.521
This paper considers a panel regression model with ill-posed data and proposes the generalized maximum entropy(GME) estimator of the unknown parameters. These are natural extensions from the biometries, statistics and econometrics literature. The performance of this estimator is investigated by using of Monte Carlo experiments. The results indicate that the GME method performs the best in estimating the unknown parameters.
Bootstrap Calibrated Confidence Bound for Variance Components Model
Lee, Yong-Hee ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 535~544
DOI : 10.5351/KJAS.2006.19.3.535
We consider use of Bootstrap calibration in the problem of setting a confidence interval for a linear combination of variance components. Based on the the modified large sample(MLS) method by Graybill and Wang(1980), Bootstrap Calibration is applied to improve the coverage probability of the MLS confidence bound when the experiment is balanced and coefficients of a linear combination are positive. Performance of the proposed confidence bound in small sample is investigated by simulation studies.
Generating Multidimensional Random Tables
Choi, Hyun-Jip ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 545~554
DOI : 10.5351/KJAS.2006.19.3.545
We suggest a method for generating multidimensional random tables based on the log-linear models. A linear combination approach by Lee(1997) is applied to get the joint distribution with the well known Pearson chi-squared statistics. We can generate completely associated joint distributions which have the fixed association among three variables by using the suggested method. Therefore the method can be extended to more higher dimension than the three dimensional tables.
Nonparametric Multiple Comparison Procedure Using Alignment Method Under Randomized Block Design
Han, Ji-Ung ; Kim, Dong-Jae ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 555~564
DOI : 10.5351/KJAS.2006.19.3.555
Friedman rank-sum multiple comparison procedure is often applied to nonparametric multiple comparison method under randomized block design. Since this method does not use between-block information, we propose, in this paper, nonparametric multiple comparison procedures employing aligned method suggested by Hedges and Lehmann(1962) under randomized block design. The proposed procedure and Friedman procedure are compared by Monte Carlo simulation study.
Trend Comparison of Repeated Measures Data between Two Groups
Hwang, Kum-Na ; Kim, Dong-Jae ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 565~578
DOI : 10.5351/KJAS.2006.19.3.565
Repeated measurement data between two group is often used in the field of medicine study. In this paper, we suggest a method for comparison of the trend between two groups based on repeated measurement data. First, we estimate regression coefficient of linear regression model from each subject and generate samples using the regression coefficient estimated previous. And then, we test the difference between two groups by unpaired t-test, Wilcoxon rank sum test and placement test using generated samples. Monte Carlo Simulation is adapted to examine the power and experimental significance levels of several methods in various combinations.
Analysis of a Ruin Model with Surplus Following a Brownian Motion
Han, Soo-Hee ; Lee, Eui-Yong ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 579~585
DOI : 10.5351/KJAS.2006.19.3.579
We consider a ruin model where the surplus process is formed by a Brownian motion. If the level of surplus exceeds V, then we assume that a insurer invests an amount of S to other place. In this paper, we apply martingale methods to the surplus process and obtain the expectation of period T, time from origin to the point where the level of surplus reaches either V or 0. As a consequence, we finally derive the total and average amount of surplus during T.
Local Linear Logistic Classification of Microarray Data Using Orthogonal Components
Baek, Jang-Sun ; Son, Young-Sook ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 587~598
DOI : 10.5351/KJAS.2006.19.3.587
The number of variables exceeds the number of samples in microarray data. We propose a nonparametric local linear logistic classification procedure using orthogonal components for classifying high-dimensional microarray data. The proposed method is based on the local likelihood and can be applied to multi-class classification. We applied the local linear logistic classification method using PCA, PLS, and factor analysis components as new features to Leukemia data and colon data, and compare the performance of the proposed method with the conventional statistical classification procedures. The proposed method outperforms the conventional ones for each component, and PLS has shown best performance when it is embedded in the proposed method among the three orthogonal components.
Spatial-Temporal Moving Sequence Pattern Mining
Han, Seon-Young ; Yong, Hwan-Seung ;
Korean Journal of Applied Statistics, volume 19, issue 3, 2006, Pages 599~617
DOI : 10.5351/KJAS.2006.19.3.599
Recently many LBS(Location Based Service) systems are issued in mobile computing systems. Spatial-Temporal Moving Sequence Pattern Mining is a new mining method that mines user moving patterns from user moving path histories in a sensor network environment. The frequent pattern mining is related to the items which customers buy. But on the other hand, our mining method concerns users' moving sequence paths. In this paper, we consider the sequence of moving paths so we handle the repetition of moving paths. Also, we consider the duration that user spends on the location. We proposed new Apriori_msp based on the Apriori algorithm and evaluated its performance results.