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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
A Decision-support System for Care Plan in Long-term Care Insurance
Han, Eun-Jeong ; Lee, Jung-Suk ; Kim, Dong-Geon ; Kwon, Jinhee ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 667~679
DOI : 10.5351/KJAS.2014.27.5.667
National Health Insurance Service(NHIS) provide care-plans for beneficiaries in the long-term care insurance(LTCI) systems that help them use LTC services appropriately. The care-plan includes recommendations for the most adequate type of care (gold standard) for beneficiaries. This study develops a decision-support system to determine the appropriate type of care plan. To develop a model, we used a data set that well-trained assessors in the NHIS investigated as a gold standard for beneficiaries: nursing home care, home-visit care, home-visit bathing, home-visit nursing, or day and night care. The decision-support system was established through a decision-tree model, because it may be easy to explain the algorithm of a decision-support system to working groups and policy makers. Our results might be useful in evidence-based care planning in an LTCI system and contribute to the efficient use of LTC services.
An Empirical Comparison of Predictability of Ranking-based and Choice-based Conjoint Analysis
Kim, Bu-Yong ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 681~691
DOI : 10.5351/KJAS.2014.27.5.681
Ranking-based conjoint analysis(RBCA) and choice-based conjoint analysis(CBCA) have attracted significant interest in various fields such as marketing research. When conducting research, the researcher has to select one suitable approach in consideration of strengths and weaknesses. This article performs an empirical comparison of the predictability of RBCA and CBCA in order to provide criterion for the selection. A new concept of measurement set is developed by combining the ranking set and choice set. The measurement set enables us to apply two approaches separately on the same consumer group that allows a fair comparison of predictability. RBCA and CBCA are conducted on consumer preferences for RTD-coffee; subsequently, the predicted values of market shares and hit rates are compared. The study result reveals that their predictabilities are not significantly different. Further, the result indicates that RBCA is recommended if the researcher wants to improve data quality by filtering out poor responses or to implement the market segmentation. In contrast, CBCA is recommended if the researcher wants to lessen the burden on the respondents or to measure preferences under similar conditions with the actual marketplace.
Some Criteria for Optimal Experimental Design at Multiple Extrapolation Points
Kim, YoungIl ; Jang, Dae-Heung ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 693~703
DOI : 10.5351/KJAS.2014.27.5.693
When setting up an experiment for extrapolation at multiple points outside the design space, we often face a difficulty in which point we should emphasize even if the polynomial model under consideration is given. In this paper we propose various methods under two possible scenarios that deal with extrapolations. One considered in this paper is the situation when the model assumed can be extended beyond the design space. In this setting, the many classical methods(including various approaches the authors proposed before) were revisited in the context of extrapolation. But the real problem arises when there is an uncertainty concerning the validity of the assumed model. Therefore, the second scenario is to develop an appropriate procedure when we have limited information about model. Consequently, a hybrid approach is suggested to deal with this issue of how to handle the multiple extrapolating under model uncertainty. A search algorithm was implemented because the classical exchange algorithm was found difficult to handle the complexity of the problem.
Modeling of Metabolic Syndrome Using Bayesian Network
Jin, Mi-Hyun ; Kim, Hyun-Ji ; Lee, Jea-Young ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 705~715
DOI : 10.5351/KJAS.2014.27.5.705
Metabolic syndrome is a major factor for cardiovascular disease that can develop into a variety of complications such as stroke disease. This study utilizes a Bayesian network to model metabolic syndrome. In addition, we tried to find the best risk combinations to diagnose metabolic syndrome. We confirmed that the combinations are difference according to individual characteristics. The paper used data from 4,489 adults who responded to all health interview questions from the the
Korea National Health and Nutrition Examination Survey conducted in 2010.
Comparison of Variable Importance Measures in Tree-based Classification
Kim, Na-Young ; Lee, Eun-Kyung ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 717~729
DOI : 10.5351/KJAS.2014.27.5.717
Projection pursuit classification tree uses a 1-dimensional projection with the view of the most separating classes in each node. These projection coefficients contain information distinguishing two groups of classes from each other and can be used to calculate the importance measure of classification in each variable. This paper reviews the variable importance measure with increasing interest in line with growing data size. We compared the performances of projection pursuit classification tree with those of classification and regression tree(CART) and random forest. Projection pursuit classification tree are found to produce better performance in most cases, particularly with highly correlated variables. The importance measure of projection pursuit classification tree performs slightly better than the importance measure of random forest.
Explanation of Run Productivity Using Weighted Adjusted OPS in Korean Professional Baseball
Kim, Hyuk Joo ; Kim, Yea Hyoung ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 731~741
DOI : 10.5351/KJAS.2014.27.5.731
We suggested an adjusted OPS and weighted adjusted OPS as indices to explain run productivity of teams using the slugging average and adjusted OBP from Korean professional baseball. First, we defined adjusted OBP by modifying currently used OBP. Next, we defined adjusted OPS as the sum of adjusted OBP and slugging average. We also defined weighted adjusted OPS as the weighted average of adjusted OBP and slugging average. Analysis of the data from all games in the regular seasons from 1982~2013 shows that adjusted OPS better explains runs than OPS. For 25 seasons out of 32 seasons, adjusted OPS explains runs better than OPS. Further, weighted adjusted OPS consisting of adjusted OBP (with weight 60%) and slugging average (with weight 40%) gives the best explanation of run productivity. Weighted adjusted OPS has been found to explain run productivity better than weighted OPS proposed in Kim (2012).
Reanalysis of 2002 Donation Frequency Data: Corrections and Supplements
Kim, Byung Soo ; Lee, Juhyung ; Kim, Inyoung ; Park, Su-Bum ; Park, Tae-Kyu ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 743~753
DOI : 10.5351/KJAS.2014.27.5.743
Kim et al. (2006) and Kim et al. (2009) reported a set of explanatory variables affecting donation frequency when they analyzed nationwide survey data on donations collected in 2002 by Volunteer 21, a nonprofit organization in Korea. The primary purpose of this paper is to correct computational errors found in Kim et al. (2006) and Kim et al. (2009), to rectify major results in the Tables and Figures and to supplement Kim et al. (2009) by providing new results. We add two logistic regressions to the ZIP and a mixture of two Poisson regressions of Kim et al. (2009). Through these two logistic regressions we could detect a set of explanatory variables affecting donation activity (0 or 1) and another set of explanatory variables, in which the volunteer (0, 1) variable is common, discriminating the infrequent donor group from the frequent donor group.
On the Geometric Anisotropy Inherent In Spatial Data
Go, Hye Ji ; Park, Man Sik ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 755~771
DOI : 10.5351/KJAS.2014.27.5.755
Isotropy is one of the main assumptions for the ease of spatial prediction (named kriging) based on some covariance models. A lack of isotropy (or anisotropy) in a spatial process necessitates that some additional parameters (angle and ratio) for anisotropic covariance model be obtained in order to produce a more reliable prediction. In this paper, we propose a new class of geometrically extended anisotropic covariance models expressed as a weighted average of some geometrically anisotropic models. The maximum likelihood estimation method is taken into account to estimate the parameters of our interest. We evaluate the performances of our proposal and compare it with an isotropic covariance model and a geometrically anisotropic model in simulation studies. We also employ extended geometric anisotropy to the analysis of real data.
Alternative Optimal Threshold Criteria: MFR
Hong, Chong Sun ; Kim, Hyomin Alex ; Kim, Dong Kyu ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 773~786
DOI : 10.5351/KJAS.2014.27.5.773
We propose the multiplication of false rates (MFR) which is a classification accuracy criteria and an area type of rectangle from ROC curve. Optimal threshold obtained using MFR is compared with other criteria in terms of classification performance. Their optimal thresholds for various distribution functions are also found; consequently, some properties and advantages of MFR are discussed by comparing FNR and FPR corresponding to optimal thresholds. Based on general cost function, cost ratios of optimal thresholds are computed using various classification criteria. The cost ratios for cost curves are observed so that the advantages of MFR are explored. Furthermore, the de nition of MFR is extended to multi-dimensional ROC analysis and the relations of classification criteria are also discussed.
Poisson GLR Control Charts
Lee, Jaeheon ; Park, Jongtae ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 787~796
DOI : 10.5351/KJAS.2014.27.5.787
Situations where sample size is not constant are common when monitoring a process with Poisson count data. In this paper, we propose a generalized likelihood ratio(GLR) control chart to detect shifts in the Poisson rate when the sample size varies. The performance of the proposed GLR chart is compared with the performance of several cumulative sum(CUSUM) type charts. It is shown that the overall performance of the GLR chart is comparable with CUSUM type charts and is significantly better in cases where the actual value of the shift is different from the pre-specified value in CUSUM type charts.
Statistical Interrelationships Among Variations in Stock Price System by Corporate Governance
Kim, Tae-Ho ; Kim, Min-Jeong ; Lee, Seung-Eun ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 797~808
DOI : 10.5351/KJAS.2014.27.5.797
It is increasingly interested in investigating the relationships and the dynamic characteristics of variations among high class corporate values. This study formulates a statistical model of simultaneous equation system to examine the relationships among variations of stock returns for each class of corporate governance structure and to analyze the dynamic patterns of their long-run adjustment processes. Changes in stock returns for each class of corporate governance by an exogenous shock are found to have common structural features of slow adjustments to the long-run equilibriums.
Saddlepoint Approximation to the Linear Combination Based on Multivariate Skew-normal Distribution
Na, Jonghwa ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 809~818
DOI : 10.5351/KJAS.2014.27.5.809
Multivariate skew-normal distribution(distribution that includes multivariate normal distribution) has been recently applied to many application areas. We consider saddlepoint approximation for a statistic of linear combination based on a multivariate skew-normal distribution. This approach can be regarded as an extension of Na and Yu (2013) that dealt saddlepoint approximation for the distribution of a skew-normal sample mean for a linear statistic and multivariate version. Simulations results and examples with real data verify the accuracy and applicability of suggested approximations.
An Alternating Approach of Maximum Likelihood Estimation for Mixture of Multivariate Skew t-Distribution
Kim, Seung-Gu ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 819~831
DOI : 10.5351/KJAS.2014.27.5.819
The Exact-EM algorithm can conventionally fit a mixture of multivariate skew distribution. However, it suffers from highly expensive computational costs to calculate the moments of multivariate truncated t-distribution in E-step. This paper proposes a new SPU-EM method that adopts the AECM algorithm principle proposed by Meng and van Dyk (1997)'s to circumvent the multi-dimensionality of the moments. This method offers a shorter execution time than a conventional Exact-EM algorithm. Some experments are provided to show its effectiveness.
Nonparametric Method for a Non-inferiority Test using Confidence Interval
Park, Sujung ; Kim, Dongjae ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 833~842
DOI : 10.5351/KJAS.2014.27.5.833
Non-inferiority trials indicate whether the effect of an experimental treatment is not worse than an active control. Chen et al. (2006) and Kang (2010) proposed a test method for non-inferiority trials using confidence intervals. In this paper, we suggest a new nonparametric method using a confidence interval based on Wilcoxon rank-sum test and Hodges-Lehmann estimator of active control. A Monte-Carlo simulation study compares the type I error and the power of the proposed method with previous methods.
Electricity Demand Forecasting for Daily Peak Load with Seasonality and Temperature Effects
Jung, Sang-Wook ; Kim, Sahm ;
Korean Journal of Applied Statistics, volume 27, issue 5, 2014, Pages 843~853
DOI : 10.5351/KJAS.2014.27.5.843
Accurate electricity demand forecasting for daily peak load is essential for management and planning at electrical facilities. In this paper, we rst, introduce the several time series models that forecast daily peak load and compare the forecasting performance of the models based on Mean Absolute Percentage Error(MAPE). The results show that the Reg-AR-GARCH model outperforms other competing models that consider Cooling Degree Day(CDD) and Heating Degree Day(HDD) as well as seasonal components.