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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 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
Estimation of the Expected Loss per Exposure of Export Insurance using GLM
Ju, Hyo Chan ; Lee, Hangsuck ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 857~871
DOI : 10.5351/KJAS.2013.26.6.857
Export credit insurance is a policy tool for export growth. In the era of free trade under the governance of WTO, export credit insurance is still allowed as one of the few instruments to increase exports. This paper, using data on short-term export insurance contracts issued to foreign subsidiaries of Korean companies, calculates the expected loss per exposure by combining the effect of risk factors (credit rate of foreign importers, size of mother company, and payment period) on loss frequency and loss severity in different levels. We, applying generalized linear models (GLM), first fit loss frequency and loss severity to negative binomial and lognormal distribution, respectively, and then estimate the loss frequency rate per contract and the ratio of loss severity to coverage amount. Finally, we calculate the expected loss per exposure for each level of risk factors by combining these two rates. Based on the result of statistical analysis, we present the implication for the current premium rate of export insurance.
Comparative Study on Axes of Rotation Data by Within-Subjects Designs
Kim, Jinuk ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 873~887
DOI : 10.5351/KJAS.2013.26.6.873
The axis of rotation in biomechanics is a major tool to investigate joint function; therefore, many methods to estimate the axis of rotation have been developed. However, there exist several problems to describe, estimate, and test the axis statistically. The axis is directional data(axial data) and it should not be analyzed with traditional statistics. A proper comparative method should be considered to compare axis estimating methods for the same given data ANOVA (analysis of variance) is a frequently used statistical method to compare treatment means in experimental designs. In case of the axial data response assumed to come from Watson distribution, there are a few ANOVA method options. This study constructed ANOVA models for within-subjects designs of axial data. Two models (one within-subjects factor and two within-subjects factors crossed design) were considered. The empirical data used in this study were instantaneous axes of rotation of flexion/extension at the knee joint and the flexion/extension and pronation/supination at the elbow joint. The results of this study can be further applied to the various analysis of experimental designs.
Comparison of Goodness-of-Fit Tests using Grouping Strategies for Multinomial Logit Regression Model
Song, Mi Kyung ; Jung, Inkyung ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 889~902
DOI : 10.5351/KJAS.2013.26.6.889
Several goodness-of-fit test statistics have been proposed for a multinomial logit regression model; however, the properties of the proposed tests were not adequately studied. This paper evaluates three different goodness-of-fit tests using grouping strategies, proposed by Fagerland et al. (2008), Bull (1994), and Pigeon and Heyse (1999). In addition, Pearson (1900)`s method is also examined as a reference. Simulation studies were conducted to evaluate the four methods in terms of null distribution and power. A real data example is presented to illustrate the methods.
Detection of the Change in Blogger Sentiment using Multivariate Control Charts
Moon, Jeounghoon ; Lee, Sungim ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 903~913
DOI : 10.5351/KJAS.2013.26.6.903
Social network services generate a considerable amount of social data every day on personal feelings or thoughts. This social data provides changing patterns of information production and consumption but are also a tool that reflects social phenomenon. We analyze negative emotional words from daily blogs to detect the change in blooger sentiment using multivariate control charts. We used the all the blogs produced between 1 January 2008 and 31 December 2009. Hotelling`s T-square control chart control chart is commonly used to monitor multivariate quality characteristics; however, it assumes that quality characteristics follow multivariate normal distribution. The performance of a multivariate control chart is affected by this assumption; consequently, we introduce the support vector data description and its extension (K-control chart) suggested by Sun and Tsung (2003) and they are applied to detect the chage in blogger sentiment.
Stepwise Estimation for Multiple Non-Crossing Quantile Regression using Kernel Constraints
Bang, Sungwan ; Jhun, Myoungshic ; Cho, HyungJun ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 915~922
DOI : 10.5351/KJAS.2013.26.6.915
Quantile regression can estimate multiple conditional quantile functions of the response, and as a result, it provide comprehensive information of the relationship between the response and the predictors. However, when estimating several conditional quantile functions separately, two or more estimated quantile functions may cross or overlap and consequently violate the basic properties of quantiles. In this paper, we propose a new stepwise method to estimate multiple non-crossing quantile functions using constraints on the kernel coefficients. A simulation study are presented to demonstrate satisfactory performance of the proposed method.
Prediction of the Number of Food Poisoning Occurrences by Microbes
Yeo, In-Kwon ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 923~932
DOI : 10.5351/KJAS.2013.26.6.923
This paper proposes a method to predict the number of foodborne disease outbreaks by microbes. The weekly data of food poisoning occurrences by microbes in Korea contain many zero-valued observations and have dependency between outbreaks. In order to model both phenomena, the number of food poisonings is predicted by an autoregressive model and the probabilities of food poisoning occurrences by microbes (given the total of food poisonings) are estimated by the baseline category logit model. The predicted number of foodborne disease outbreaks by a microbe is obtained by multiplying the predicted number of foodborne disease outbreaks and the estimated probability of the food poisoning by the corresponding microbe. The mean squared error and the mean absolute value error are evaluated to compare the performances of the proposed method and the zero-inflated model.
A Comparative Study on Factor Recovery of Principal Component Analysis and Common Factor Analysis
Jung, Sunho ; Seo, Sangyun ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 933~942
DOI : 10.5351/KJAS.2013.26.6.933
Common factor analysis and principal component analysis represent two technically distinctive approaches to exploratory factor analysis. Much of the psychometric literature recommends the use of common factor analysis instead of principal component analysis. Nonetheless, factor analysts use principal component analysis more frequently because they believe that principal component analysis could yield (relatively) less accurate estimates of factor loadings compared to common factor analysis but most often produce similar pattern of factor loadings, leading to essentially the same factor interpretations. A simulation study is conducted to evaluate the relative performance of these two approaches in terms of factor pattern recovery under different experimental conditions of sample size, overdetermination, and communality.The results show that principal component analysis performs better in factor recovery with small sample sizes (below 200). It was further shown that this tendency is more prominent when there are a small number of variables per factor. The present results are of practical use for factor analysts in the field of marketing and the social sciences.
On Prediction Intervals for Binomial Data
Ryu, Jea-Bok ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 943~952
DOI : 10.5351/KJAS.2013.26.6.943
Wald, Agresti-Coull, Jeffreys, and Bayes-Laplace methods are commonly used for confidence interval of binomial proportion are applied for prediction intervals. We used coverage probability, mean coverage probability, root mean squared error, and mean expected width for numerical comparisons. From the comparisons, we found that Wald is not proper as for confidence interval and Agresti-Coull is too conservative to differ from confidence interval. However, Jeffrey and Bayes-Laplace are good for prediction interval and Jeffrey is especially desirable as for confidence interval.
Measurement Error Model with Skewed Normal Distribution
Heo, Tae-Young ; Choi, Jungsoon ; Park, Man Sik ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 953~958
DOI : 10.5351/KJAS.2013.26.6.953
This study suggests a measurement error model based on skewed normal distribution instead of normal distribution to identify slope parameter properties in a simple liner regression model. We prove that the slope parameter in a simple linear regression model is underestimated.
Good Bank Evaluation by Chernoff Face Analysis using SAS macro faces
Lee, Jeongeun ; Jeong, Hyeseon ; Kim, Minji ; Kim, Jihyun ; Son, Young Sook ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 959~975
DOI : 10.5351/KJAS.2013.26.6.959
The SAS macro faces program by Friendly (1992) is for Chernoff face analysis, which is one of methods for the visualization representation of multivariate data. In this paper, we examined 18 face features used in the program and presented the modified program depending on the definition of a good face in days present. In addition, a good bank evaluation for 15 domestic banks was performed through Chernoff face analysis based on 11 bank economic indicators representing stability, the consumer satisfaction, soundness, and banks profitability.
Standard Criterion of VUS for ROC Surface
Hong, C.S. ; Jung, E.S. ; Jung, D.G. ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 977~985
DOI : 10.5351/KJAS.2013.26.6.977
Many situations are classified into more than two categories in real world. In this work, we consider ROC surface and VUS, which are graphical representation methods for classification models with three categories. The standard criteria of AUC for the probability of default based on Basel II is extended to the VUS for ROC surface; therefore, the standardized criteria of VUS for the classification model is proposed. The ranges of AUC, K-S and mean difference statistics corresponding to VUS values for each class of the standard criteria are obtained. The standard criteria of VUS for ROC surface can be established by exploring the relationships of these statistics.
Time Series Modelling of Air Quality in Korea: Long Range Dependence or Changes in Mean?
Baek, Changryong ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 987~998
DOI : 10.5351/KJAS.2013.26.6.987
This paper considers the statistical characteristics on the air quality (PM10) of Korea collected hourly in 2011. PM10 in Korea exhibits very strong correlations even for higher lags, namely, long range dependence. It is power-law tailed in marginal distribution, and generalized Pareto distribution successfully captures the thicker tail than log-normal distribution. However, slowly decaying autocorrelations may confuse practitioners since a non-stationary model (such as changes in mean) can produce spurious long term correlations for finite samples. We conduct a statistical testing procedure to distinguish two models and argue that the high persistency can be explained by non-stationary changes in mean model rather than long range dependent time series models.
Moving Data Pictures
Huh, Myung-Hoe ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 999~1007
DOI : 10.5351/KJAS.2013.26.6.999
This research shows several types of moving pictures from the data: 1) the word cloud of Korean texts, 2) the heat map of n
p matrices, 3) the moving image of p
p scatterplot matrix, 4) the local projective display of k clusters (Huh and Lee, 2012). Moving pictures may reveal the hidden information and beauty of the datasets and ignite the curiosity of information consumers. Video files are attached.
Parallelism Test of Slope in a Several Simple Linear Regression Model based on a Sequential Slope
Kim, Juhie ; Kim, Dongjae ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 1009~1018
DOI : 10.5351/KJAS.2013.26.6.1009
Regression analysis is useful to understand the relationship of variables; however, we need to test if the slope of each regression lines is the same when comparing several populations. This paper suggests a new parallelism test for several linear regression lines. We use F-test of ANOVA and Kruskal-Wallis (1952) tests after obtaining slope estimator from a sequential slope. In addition, a Monte Carlo simulation study is adapted to compare the power of the proposed methods with those of Park and Kim (2009).
Comparison of Mortality Estimate and Prediction by the Period of Time Series Data Used
Jung, Kyunam ; Baek, Jeeseon ; Kim, Donguk ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 1019~1032
DOI : 10.5351/KJAS.2013.26.6.1019
The accurate prediction of future mortality is an important issue due to recent rapid increases in life expectancy. An accurate estimation and prediction of mortality is important to future welfare policies. The optimal selection of a mortality model is important to estimate and predict mortality; however, the period of time series data used is also an important issue. It is essential to understand that the time series data for mortality is short in Korea and the data before 1982 is incomplete. This paper divides the time series of Korean mortality into two sets to compare the parameter estimates of the LC model and LC model with a cohort effect by the period of data used. A modeling and prediction of the mortality index and cohort effect index as well as the evaluation of future life expectancy is conducted. Finally, some suggestions are proposed for the future prediction of mortality.
Efficiency of Variance Estimators for Two-stage PPS Systematic Sampling
Kim, Young-Won ; Kim, Yeny ; Han, Hye-Eun ; Kwak, Eun-Sun ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 1033~1041
DOI : 10.5351/KJAS.2013.26.6.1033
In this paper, we investigate several variance estimators for pps systematic sampling. Unfortunately, there is no unbiased variance estimators for a systematic sample because systematic sampling can be regarded as a random selection of one cluster. This study provides guidance on which variance estimator may be more appropriate than others in several circumstances. We judge the efficiency of variance estimators for systematic sampling based on of their relative biases and relative mean square error. Also, we investigate variance estimation problems for two-stage systematic sampling applied for the Food Raw Material Consumption Survey and the Establishment Labor Force Survey simulation study, in order to consider the popular two-stage pps systematic sample design for establishment and household survey in Korea.
Structural Vector Error Correction Model for Korean Labor Market Data
Seong, Byeongchan ; Jung, Hyosang ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 1043~1051
DOI : 10.5351/KJAS.2013.26.6.1043
We use a structural vector error correction model of the labor market to investigate the effect of shocks to Korean unemployment. We associate technology, labor demand, labor supply, and wage-setting shocks with equations for productivity, employment, unemployment, and real wages, respectively. Subsequently, labor demand and supply shocks have significant long-run and contemporaneous effects on unemployment, respectively.
A Study on the Short Term Internet Traffic Forecasting Models on Long-Memory and Heteroscedasticity
Sohn, H.G. ; Kim, S. ;
Korean Journal of Applied Statistics, volume 26, issue 6, 2013, Pages 1053~1061
DOI : 10.5351/KJAS.2013.26.6.1053
In this paper, we propose the time series forecasting models for internet traffic with long memory and heteroscedasticity. To control and forecast traffic volume, we first introduce the traffic forecasting models which are determined by the volatility and heteroscedasticity of the traffic. We then analyze and predict the heteroscedasticity and the long memory properties for forecasting traffic volume. Depending on the characteristics of the traffic, Fractional ARIMA model, Fractional ARIMA-GARCH model are applied and compared with the MAPE(Mean Absolute Percentage Error) Criterion.