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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 27, Issue 7 - Dec 2014
Volume 27, Issue 6 - Dec 2014
Volume 27, Issue 5 - Oct 2014
Volume 27, Issue 4 - Aug 2014
Volume 27, Issue 3 - Jun 2014
Volume 27, Issue 2 - Apr 2014
Volume 27, Issue 1 - Feb 2014
Selecting the target year
Estimating Average Causal Effect in Latent Class Analysis
Park, Gayoung ; Chung, Hwan ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1077~1095
DOI : 10.5351/KJAS.2014.27.7.1077
Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid only when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the `National Longitudinal Study of Adolescent Health`.
Analysis of Multiple Life Insurance using Copula and Common Shock
Kim, Doyoung ; Lee, Issac ; Lee, Hangsuck ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1097~1114
DOI : 10.5351/KJAS.2014.27.7.1097
Multiple-life policies pay a benefit on the first death or the last death among the group of lives. In practice, the future lifetime random variable of policy holders has been considered to be independent, but it is more rational to take into account the correlations among the policy holders. In this paper, the Gaussian copula is applied to re ect the correlations among policy holders and then to diversify the common shock of the multiple life policies which follows an exponential distribution. Five case studies demonstrate its usefulness of using copula in calculating the premiums of the multiple-life policies including the common shock.
Maximum Tolerated Dose Estimation with Dose De-Escalation Design in a Phase I Clinical Trials
Jang, Eunah ; Kim, Dongjae ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1115~1123
DOI : 10.5351/KJAS.2014.27.7.1115
The main purpose of phase I clinical trials is to estimate the Maximum Tolerated Dose (MTD), which minimizes side effect and assures safety of a new drug by evaluating the toxicity at each dose-level. The conventional MTD estimation methods is Standard method (Storer, 1989; Korn et al., 1994), Accelerated Titration Designs (Simon et al., 1997) and DM method (Dixon and Mood, 1948) etc. In this paper, MTD estimation method with de-escalation is suggested phase I clinical trials. The proposed MTD estimation method is compared to Accelerated Titration Designs, SM3 without de-escalation method and SM3 with de-escalation method using a Monte Carlo simulation.
The Analysis of Private Education Cost for the Elementary, Middle, and High School Students in Korea
Lee, Hyejeong ; Song, Jongwoo ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1125~1137
DOI : 10.5351/KJAS.2014.27.7.1125
This paper studies what affects the private education cost for the elementary, middle, and high school students. It is a big issue now because there can be a problem in the equal opportunity for education if the portion of private education cost is very high in the total education cost. If we spend more time and money on the private education than the school education, it can cause the polarization among the classes and regions. The excessive private education also can deteriorate the school system. we use various regression and classification methods to analyze the cost of private education and find the important variables in the models. we found that large cities spend more money on the private education than small cities. We also found that high school students spend more than middle school students and the elementary students and the household with more income spend more money on the private education.
Multivariate Volatility Analysis via Canonical Correlations for Financial Time Series
Lee, Seung Yeon ; Hwang, S.Y. ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1139~1149
DOI : 10.5351/KJAS.2014.27.7.1139
Multivariate volatility is summarized through canonical correlation analysis (CCA). Along with the standard CCA, non-negative and sparse canonical correlation analysis (NSCCA) is introduced to make sure that volatility coefficients are non-negative and the number of coefficients in the volatility CCA is as small as possible. Various multivariate financial time series are analyzed to illustrate the main contribution of the paper.
Some Examples of Constrained Optimal Experimental Design for Nonlinear Models
Kim, Youngil ; Jang, Dae-Heung ; Yi, Seongbaek ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1151~1161
DOI : 10.5351/KJAS.2014.27.7.1151
Despite the fact that the optimal design for nonlinear model depends on the unknown quantity of parameter to estimate basically, its popularity is growing in bio and engineering statistics area since all those models in the area are virtually nonlinear. In this paper we have dealt with the case when the researcher has multiple objectives in experimentation, decision among the competing models, protection against the departure from the assumed model, and the con icting interests among design criteria. To tackle these issues we attempted several new approaches which are taking advantage of the easiness of constrained optimal design. Several nonlinear models were tested.
Test of Homogeneity for Intermittent Panel AR(1) Processes and Application
Lee, Sung Duck ; Kim, Sun Woo ; Jo, Na Rae ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1163~1170
DOI : 10.5351/KJAS.2014.27.7.1163
The concepts and structure of intermittent panel time series data are introduced. We suggest a Wald test statistic for the test of homogeneity for intermittent panel first order autoregressive model and its limit distribution is derived. We consider the fitting the model with pooling data using sample mean at the time point if homogeneity for intermittent panel AR(1) is satisfied. We performed simulations to examine the limit distribution of the homogeneity test statistic for intermittent panel AR(1). In application, we fit the intermittent panel AR(1) for panel Mumps data and investigate the test of homogeneity.
Rhipe Platform for Big Data Processing and Analysis
Jung, Byung Ho ; Shin, Ji Eun ; Lim, Dong Hoon ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1171~1185
DOI : 10.5351/KJAS.2014.27.7.1171
Rhipe that integrates R and Hadoop environment, made it possible to process and analyze massive amounts of data using a distributed processing environment. In this paper, we implemented multiple regression analysis using Rhipe with various data sizes of actual data and simulated data. Experimental results for comparing the computing speeds of pseudo-distributed and fully-distributed modes for configuring Hadoop cluster, showed fully-distributed mode was more fast than pseudo-distributed mode and computing speeds of fully-distributed mode were faster as the number of data nodes increases. We also compared the performance of our Rhipe with stats and biglm packages available on bigmemory. The results showed that our Rhipe was more fast than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases.
Analysis of Climate Effects on Italian Ryegrass Yield via Structural Equation Model
Kim, Moonju ; Sung, Kyung-Il ; Kim, Young-Ju ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1187~1196
DOI : 10.5351/KJAS.2014.27.7.1187
Italian Ryegrass (IRG), which is known as high yielding and the highest quality winter annual forage crop, is grown in mid-south area in Korea. This study aims to analyze the cause-and-effect relationship between IRG yield and climate variables such as temperature and precipitation by using IRG data and climate data of Korea Weather Bureau. From path analysis of structural equation model under multivariate normality, we found that there was a weather effect on IRG yield that the winter grass IRG yield was directly affected by spring temperature and indirectly affected by spring rainfall. These results showed that IRG can be sown in early spring in the area where it is hard to prepare for winter due to low temperature. This paper can contribute to increase IRG yield by showing the cause-and-effect relationship and this study can be extended to various structural equation models for other crops.
Nonparmetric Method for Identifying Effective and Safe Doses using Placement
Kim, Sunhye ; Kim, Dongjae ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1197~1205
DOI : 10.5351/KJAS.2014.27.7.1197
Typical clinical dose development studies consist of the comparison of several doses of a drug with a placebo. The primary interest is to find therapeutic window that satisfying both efficacy and safety. In this paper, we propose nonparametric method for identifying effective and safe doses in linear placement using score function. The Monte Carlo simulation is adapted to estimate the power and the family-wise error rate(FWE) of proposed procedure are compared with previous methods.
Text Mining for Korean: Characteristics and Application to 2011 Korean Economic Census Data
Goo, Juna ; Kim, Kyunga ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1207~1217
DOI : 10.5351/KJAS.2014.27.7.1207
2011 Korean Economic Census is the first economic census in Korea, which contains text data on menus served by Korean-food restaurants as well as structured data on characteristics of restaurants including area, opening year and total sales. In this paper, we applied text mining to the text data and investigated statistical and technical issues and characteristics of Korean text mining. Pork belly roast was the most popular menu across provinces and/or restaurant types in year 2010, and the number of restaurants per 10000 people was especially high in Kangwon-do and Daejeon metropolitan city. Beef tartare and fried pork cutlet are popular menus in start-up restaurants while whole chicken soup and maeuntang (spicy fish stew) are in long-lived restaurants. These results can be used as a guideline for menu development to restaurant owners, and for government policy-making process that lead small restaurants to choose proper menus for successful business.
Extended Constant Conditional Correlation (ECCC) Model for Multivariate GARCH Time Series: an Illustration
Lee, Seung Yeon ; Hwang, S.Y. ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1219~1228
DOI : 10.5351/KJAS.2014.27.7.1219
Constant conditional correlation (CCC) is frequently employed for parsimony in the field of multivariate GARCH time series. An extended-CCC (ECCC) model is further developed in order to allow interactions between multivariate volatilities. The paper introduces both CCC model and ECCC model to the domestic financial time series. The CCC and ECCC models are fitted and then compared with each other through various multivatiate time series.
A Multiple Imputation for Reducing Outlier Effect
Kim, Man-Gyeom ; Shin, Key-Il ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1229~1241
DOI : 10.5351/KJAS.2014.27.7.1229
Most of sampling surveys have outliers and non-response missing values simultaneously. In that case, due to the effect of outliers, the result of imputation is not good enough to meet a given precision. To overcome this situation, outlier treatment should be conducted before imputation. In this paper in order for reducing the effect of outlier, we study outlier imputation methods and outlier weight adjustment methods. For the outlier detection, the method suggested by She and Owen (2011) is used. A small simulation study is conducted and for real data analysis, Monthly Labor Statistic and Briquette Consumption Survey Data are used.
A Study on Health-related PSR Model using Korean Working Conditions Survey Data
Kim, Youngsun ; Jo, Jinnam ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1243~1255
DOI : 10.5351/KJAS.2014.27.7.1243
This study is aimed at developing an index and indicator in the light of social factors by analyzing the basic materials on Korean working conditions survey to make it possible to grasp various working environment factors consequent on business type and to judge the industrial safety & health policy of the related area. For the purpose of developing an index, this study was conducted by benchmarking the OECD-suggested index development guidelines and overseas cases of index development. This study suggested indexes related to health by benchmarking OECD`s press-state-response model. The press-state health-related indexes specified in Korean working condtions survey were found to consist of physical risk environment, working hours, business environment, and social environment, and its consequent `state` items were comprised of mental health, physical health, absence from work due to health problems and work satisfaction as health-related items. As a result, it was found that the `press-state index` for wage worker, regular employee, manager, clerks, expert & related personnel involved, and workers aged under 50 was relatively good; in contrast, the `press-state index` for people aged over 50, owner-operator, daily job, skilled position in agriculture & fisheries, simple labor service, and apparatus & machines assembly worker was found to be relatively vulnerable.
A Study on Demand Forecasting for KTX Passengers by using Time Series Models
Kim, In-Joo ; Sohn, Hueng-Goo ; Kim, Sahm ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1257~1268
DOI : 10.5351/KJAS.2014.27.7.1257
Since the introduction of KTX (Korea Tranin eXpress) in Korea reilway market, number of passengers using KTX has been greatly increased in the market. Thus, demand forecasting for KTX passengers has been played a importantant role in the train operation and management. In this paper, we study several time series models and compare the models based on considering special days and others. We used the MAPE (Mean Absolute Percentage Errors) to compare the performance between the models and we showed that the Reg-AR-GARCH model outperformanced other models in short-term period such as one month. In the longer periods, the Reg-ARMA model showed best forecasting accuracy compared with other models.
The Maximin Robust Design for the Uncertainty of Parameters of Michaelis-Menten Model
Kim, Youngil ; Jang, Dae-Heung ; Yi, Seongbaek ;
Korean Journal of Applied Statistics, volume 27, issue 7, 2014, Pages 1269~1278
DOI : 10.5351/KJAS.2014.27.7.1269
Despite the D-optimality criterion becomes very popular in designing an experiment for nonlinear models because of theoretical foundations it provides, it is very critical that the criterion depends on the unknown parameters of the nonlinear model. But some nonlinear models turned out to be partially nonlinear in sense that the optimal design depends on the subset of parameters only. It was a strong belief that the maximin approach to find a robust design to protect against the uncertainty of parameters is not guaranteed to be successful in nonlinear models. But the maximin approach could be a success for the partial nonlinear model, because often the optimal design depends on only one unknown value of parameter, easier to handle than the full parameters. We deal with maximin approach for Michaelis-Menten model with respect to D- and