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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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The Korean Statistical Society
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Volume & Issues
Volume 20, Issue 3 - Nov 2007
Volume 20, Issue 2 - Jul 2007
Volume 20, Issue 1 - Mar 2007
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A Study for Building Credit Scoring Model using Enterprise Human Resource Factors
Lee, Yung-Seop ; Park, Joo-Wan ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 423~440
DOI : 10.5351/KJAS.2007.20.3.423
Although various models have been developed to establish the enterprise credit scoring, no model has utilized the enterprise human resource so far. The purpose of this study was to build an enterprise credit scoring model using enterprise human resource factors. The data to measure the enterprise credit score were made by the first-year research material of HCCP was used to investigate the enterprise human resource and 2004 Credit Rating Score generated from KIS-Credit Scoring Model. The independent variables were chosen among questionnaires of HCCP based on Mclagan(1989)`s HR wheel model, and the credit score of Korean Information Service was used for the dependent variables. The statistical method used for data analysis was logistic regression. As a result of constructing a model, 22 variables were selected. To see these specifically by each large area, 6 variables in human resource development(HRD) area, 15 in human resource management(HRM) area, and 1 in the other area were chosen. As a consequence of 10 fold cross validation, misclassification rate and G-mean were 30.81 and 68.27 respectively. Decile having the highest response rate was bigger than the one having the lowest response rate by 6.08 times, and had a tendency to decrease. Therefore, the result of study showed that the proposed model was appropriate to measure enterprise credit score using enterprise human resource variables.
Application of Artificial Neural Network for Conjoint Analysis
Pak, Ro-Jin ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 441~447
DOI : 10.5351/KJAS.2007.20.3.441
The conjoint analysis is widely accepted in the field of marketing as a way to understand and incorporate the structure of customer preferences into the new product design process. We apply the conjoint analysis for understanding preferences about after school computer courses in elementary schools. We show that the artificial neural network analysis in addition to the conjoint analysis is very useful to understand the needs of elementary school students about after school computer courses.
The Application of Multi-State Model to the Bipolar Disorder Study
Kim, Yang-Jin ; Kang, Si-Hyun ; Kim, Chang-Yoon ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 449~458
DOI : 10.5351/KJAS.2007.20.3.449
Bipolar disorder, also known as manic-depressive illness, is a brain disorder that causes unusual shifts in person`s mood, energy, and ability to function. Compared with manic episode, the depression episode causes more serious results such as restless, loss of interest or pleasure, or thoughts of death or suicide and the cure rate of depression episode is lower than that of manic episode. Furthermore, a long term use of antidepressants in bipolar patients may result in manic episode. Our interest is to investigate the effect of antidepressant on switch of moods of bipolar patients and to estimate the transition probabilities of switch between moods, depression and (hypo) manic. In this study, three approaches are applied in terms of multi state model. Parametric model is applied using left censoring data and nonparametric model is implemented under illness-death model with counting process. In order to estimate the effect of covariates, a multiplicative model is used. These all methods have similar results.
A Study on the Prediction of Traffic Counts Based on Shortest Travel Path
Heo, Tae-Young ; Park, Man-Sik ; Eom, Jin-Ki ; Oh, Ju-Sam ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 459~473
DOI : 10.5351/KJAS.2007.20.3.459
In this paper, we suggest a spatial regression model to predict AADT. Although Euclidian distances between one monitoring site and its neighboring sites were usually used in the many analysis, we consider the shortest travel path between monitoring sites to predict AADT for unmonitoring site using spatial regression model. We used universal Kriging method for prediction and found that the overall predictive capability of the spatial regression model based on shortest travel path is better than that of the model based on multiple regression by cross validation.
A Multivariate Analysis of Variance Applied to the Subjective Test of the Sound Quality of the Car Audio
Choi, Kyung-Mee ; Doo, Se-Jin ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 475~485
DOI : 10.5351/KJAS.2007.20.3.475
In this work we measured and analyzed the subjective opinions of consumers towards the sound quality of car audios through a questionnaire. First of all, we chose eight controllable factors which had been known to affect the quality of reproduced sound. An orthogonal design of experiments was used to imitate the objective sound environments by reproducing the combinations of 8 sound characteristics, each with two levels. Then we defined 8 corresponding response variables to measure the subjective opinions towards the quality of reproduced sound. Finally, we applied the Multivariate Analysis of Valiance to explore the significant sound characteristics which affected the subjective opinions towards the quality of reproduced sound.
Squared Log-return and TGARCH Model : Asymmetric Volatility in Domestic Time Series
Park, J.A. ; Song, Y.J. ; Baek, J.S. ; Hwang, S.Y. ; Choi, M.S. ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 487~497
DOI : 10.5351/KJAS.2007.20.3.487
As is pointed out by Gourieroux (1997), the volatility effects in financial time series vary according to the signs of the return rates and therefore asymmetric Threshold-GARCH (TGARCH, henceforth) processes are natural extensions of the standard GARCH toward asymmetric volatility modeling. For preliminary detection of asymmetry in volatility, we suggest graphs of squared-log-returns for various financial time series including KOSPI, KOSDAQ and won-Euro exchange rate. Next, asymmetric TGARCH(1,1) model fits are provided in comparisons with standard GARCH(1.1) models.
Imputation Method using the Space-Time Model in Sample Survey
Lee, Jin-Hee ; Shin, Key-Il ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 499~514
DOI : 10.5351/KJAS.2007.20.3.499
It is a common practice to use the auxiliary variables to impute missing values from item nonresponse in surveys. Sometimes there are few auxiliary variables for missing value imputation, but if spatial and time autocorrelations exist, we should use these correlations for better results. Recently, Lee et al. (2006) showed that spatial autocorrelation could be efficiently used for missing value imputation when spatial autocorrelation existed, using the data from the farm household economy data in Gangwon-do, 2002. In this paper, we present au evaluation of spatial and space-time nonresponse imputation methods when there exist spatial and time autocorrelations using the monthly data during 2000-2002 from the same data previously used by Lee et al. (2006). We show that space-time imputation method is more efficient than the other through the numerical simulations.
Determination of the Optimal Cutoff Point using Adjusted Stratum-Specific Likelihood Ratios when Disease Verification is subject to Verification Bias
Kim, Hu-Nam ; Park, Yong-Gyu ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 515~530
DOI : 10.5351/KJAS.2007.20.3.515
Stratum-specific likelihood ratio, which is ratio of the sensitivity to 1-the specificity in each stratum of the test, could be biased if the sensitivity and specificity of diagnostic test are affected by verification bias. Therefore, the optimal cutoff point determined by biased stratum-specific likelihood ratios is incorrect. In this study, we derived adjusted stratum-specific likelihood ratios using the adjusted sensitivity and specificity, and obtained the adjusted optimal cutoff point. The influence of the verification bias on the optimal cutoff point was described through the relation between adjusted and unadjusted stratum-specific likelihood ratios.
Gene Selection Based on Support Vector Machine using Bootstrap
Song, Seuck-Heun ; Kim, Kyoung-Hee ; Park, Chang-Yi ; Koo, Ja-Yong ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 531~540
DOI : 10.5351/KJAS.2007.20.3.531
The recursive feature elimination for support vector machine is known to be useful in selecting relevant genes. Since the criterion for choosing relevant genes is the absolute value of a coefficient, the recursive feature elimination may suffer from a scaling problem. We propose a modified version of the recursive feature elimination algorithm using bootstrap. In our method, the criterion for determining relevant genes is the absolute value of a coefficient divided by its standard error, which accounts for statistical variability of the coefficient. Through numerical examples, we illustrate that our method is effective in gene selection.
Prediction Interval Estimation in Ttansformed ARMA Models
Cho, Hye-Min ; Oh, Sung-Un ; Yeo, In-Kwon ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 541~550
DOI : 10.5351/KJAS.2007.20.3.541
One of main aspects of time series analysis is to forecast future values of series based on values up to a given time. The prediction interval for future values is usually obtained under the normality assumption. When the assumption is seriously violated, a transformation of data may permit the valid use of the normal theory. We investigate the prediction problem for future values in the original scale when transformations are applied in ARMA models. In this paper, we introduce the methodology based on Yeo-Johnson transformation to solve the problem of skewed data whose modelling is relatively difficult in the analysis of time series. Simulation studies show that the coverage probabilities of proposed intervals are closer to the nominal level than those of usual intervals.
Algorithm for the Robust Estimation in Logistic Regression
Kim, Bu-Yong ; Kahng, Myung-Wook ; Choi, Mi-Ae ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 551~559
DOI : 10.5351/KJAS.2007.20.3.551
The maximum likelihood estimation is not robust against outliers in the logistic regression. Thus we propose an algorithm for the robust estimation, which identifies the bad leverage points and vertical outliers by the V-mask type criterion, and then strives to dampen the effect of outliers. Our main finding is that, by an appropriate selection of weights and factors, we could obtain the logistic estimates with high breakdown point. The proposed algorithm is evaluated by means of the correct classification rate on the basis of real-life and artificial data sets. The results indicate that the proposed algorithm is superior to the maximum likelihood estimation in terms of the classification.
Robust Interpolation Method for Adapting to Sparse Design in Nonparametric Regression
Park, Dong-Ryeon ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 561~571
DOI : 10.5351/KJAS.2007.20.3.561
Local linear regression estimator is the most widely used nonparametric regression estimator which has a number of advantages over the traditional kernel estimators. It is well known that local linear estimator can produce erratic result in sparse regions in the realization of the design and the interpolation method of Hall and Turlach (1997) is the very efficient way to resolve this problem. However, it has been never pointed out that Hall and Turlach`s interpolation method is very sensitive to outliers. In this paper, we propose the robust version of the interpolation method for adapting to sparse design. The finite sample properties of the method is compared with Hall and Turlach`s method by the simulation study.
Korea-specified Maximum Expected Utility Model for the Probability of Default
Park, You-Sung ; Song, Ji-Hyun ; Choi, Bo-Seung ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 573~584
DOI : 10.5351/KJAS.2007.20.3.573
A well estimated probability of default is most important for constructing a good credit scoring process. The maximum expected utility (MEU) model has been suggested as an alternative of the traditional logistic regression model. Because the MEU model has been constructed using financial data arising from North America and European countries, the MEU model may not be suitable to Korean private firms. Thus, we propose a Korea-specific MEU model by estimating the parameters involved in kernel functions. This Korea-specific MEU model is illustrated using 34,057 private firms to show the performance of the MEU model relative to the usual logistic regression model.
The Transform of Multidimensional Categorical Data and its Applications
Ahn, Ju-Sun ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 585~595
DOI : 10.5351/KJAS.2007.20.3.585
The squared Euclid distance of the values which is transformed by P-matrix of Ahn et al. (2003) is in proportion to the squared Euclid distance of cell`s relative frequencies in two Contingency Tables. We propose the method of using the PP-values for the analysis of modern poems and questionnaire data.
Design of Variable Life-Adjusted Display (VLAD) Charts
Lee, Jae-Heon ; Jung, Sang-Hyun ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 597~604
DOI : 10.5351/KJAS.2007.20.3.597
There are many uses of control charts in health-care monitoring and in public-health surveillance. For example, control charts are used in monitoring and improvement of hospital performance, in monitoring chronic diseases and infectious diseases, and so on. We introduce the Variable Life-Adjusted Display (VLAD) chart and propose the method for choosing control limits of the VLAD chart to give specified in-control properties.
Determination of Sample Size and Comparison of Efficiency in Adaptive Cluster Sampling
NamKung, Pyong ; Won, Hye-Kyoung ; Choi, Jae-Hyuk ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 605~618
DOI : 10.5351/KJAS.2007.20.3.605
Adaptive sampling design is the selection procedure which depends on observed values of the variable of interest. It is the method which could be applied to the rare and unapproachable population. Adaptive cluster sampling strategies are more efficient than simple random sampling on equivalent sample size. Adaptive sampling with new estimators through the Rao-blackwell method have lower variance than Horvitz-Thompson (HT) and Hansen-Hurwitz (HH). Also, to determine suitable sample size, it was used expected sample and the method finding appropriate sample size by changing initial sample size were studied.
Teaching Statistical Graphics using R
Park, Dong-Ryeon ;
Korean Journal of Applied Statistics, volume 20, issue 3, 2007, Pages 619~634
DOI : 10.5351/KJAS.2007.20.3.619
It is well known that graphical display is critical to data analysis. A lot of research for data visualization has been done, so many effective graphical tools are now available. With the proper use of these graphical tools, we can penetrate the complex structure of data set easily. To enjoy the benefit of the powerful graphical display, the choice of the statistical software is very crucial. R is a popular open source software tool for statistical analysis and graphics, and can provide the very powerful graphics facilities. Moreover, many researchers believe that R is the best software for statistical graphics. In this paper, we would like to discuss what we teach and how we teach in statistical graphics course using R.