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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Communications for Statistical Applications and Methods
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Journal DOI :
The Korean Statistical Society
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Volume & Issues
Volume 18, Issue 6 - Nov 2011
Volume 18, Issue 5 - Sep 2011
Volume 18, Issue 4 - Jul 2011
Volume 18, Issue 3 - May 2011
Volume 18, Issue 2 - Mar 2011
Volume 18, Issue 1 - Jan 2011
Selecting the target year
A Statistical Analysis on Temperature Change and Climate Variability in Korea
Kim, Hyun-Chul ; Choi, Seung-Kyung ; Yun, Bo-Ra ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 1~12
DOI : 10.5351/CKSS.2011.18.1.001
We analyzed the observed temperature data for 50 years on 5 representative points in Korea to verify global warming and the increase in climate variability. We found that there was some level of global warming but we could not disregard the effects of urbanization. In addition, we could not find any information for the increase in climate variability.
Comparison of Forecasting Performance in Multivariate Nonstationary Seasonal Time Series Models
Seong, Byeong-Chan ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 13~21
DOI : 10.5351/CKSS.2011.18.1.013
This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.
A Study for the Development of a Bid Price Rate Prediction Model
Choi, Bo-Seung ; Kang, Hyun-Cheol ; Han, Sang-Tae ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 23~34
DOI : 10.5351/CKSS.2011.18.1.023
Property auctions have become a new method for real estate investment because the property auction market grows in tandem with the growth of the real estate market. This study focused on the statistical model for predicting bid price rates which is the main index for participants in the real estate auction market. For estimating the monthly bid price rate, we proposed a new method to make up for the mean of regions and terms as well as to reduce the prediction error using a decision tree analysis. We also proposed a linear regression model to predict a bid price rate for individual auction property. We applied the proposed model to apartment auction property and tried to predict the bid price rate as well as categorize individual auction property into an auction grade.
Problems in Mandatory Course Evaluations
Han, Kyung-Soo ; Choi, Sook-Hee ; Park, Jae-Cheol ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 35~45
DOI : 10.5351/CKSS.2011.18.1.035
Some researchers insist that many students respond to the course evaluation surveys without sincerity and even without reading the questions. To analyze the pattern of student responses, the results of course evaluations for five semesters at Jeonbuk National University are reviewed. In mandatory course evaluations, 20% of the students marked the same option numbers to all questions regarding their lectures. In addition, consistent responses were over 50%. These results show that the university administration should reform the current course evaluation system in all respects.
Doubly Robust Imputation Using Auxiliary Information
Park, Hyeon-Ah ; Jeon, Jong-Woo ; Na, Seong-Ryong ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 47~55
DOI : 10.5351/CKSS.2011.18.1.047
Ratio and regression imputations depend on the model of a survey variable and the relation between the survey variable and auxiliary variables. If the model is not true, the unbiasedness of the estimator using the ratio or regression imputation cannot be guaranteed. In this paper, we develop the doubly robust imputation, which satisfies the approximate unbiasedness of the estimator, whether the model assumption is valid or not. The proposed imputation increases the efficiency of estimation by using the population information of the auxiliary variables. The simulation study establishes the theoretical results of this paper.
Multivariate Meta-Analysis Methods of Comparing the Sensitivity and Specificity of Two Diagnostic Tests
Nam, Seon-Young ; Song, Hae-Hiang ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 57~69
DOI : 10.5351/CKSS.2011.18.1.057
Researchers are continuously trying to find innovative diagnostic tests and published articles are accumulating at an enormous rate in many medical fields. Meta-analysis enables previously published study results to be reviewed and summarized; therefore, an objective assessment of diagnostic tests can be done with a meta-analysis of sensitivities and specificities. Data obtained by applying two diagnostic tests to a well-defined group of diseased patients produce a pair of sensitivity and by applying the same medical tests to a group of non-diseased subjects produce a pair of specificity. The statistical tests in the meta-analysis need to consider the correlatedness of the results from two diagnostic tests applied to the same diseased and non-diseased subjects. The associations between two diagnostic test results are often found to be unequal for the diseased and non-diseased subjects. In this paper, multivariate meta-analytic methods are studied by taking into account the different associations between correlated variables. On the basis of Monte Carlo simulations, we evaluate the performance of the multivariate meta-analysis methods proposed in this paper.
Asymptotics in Transformed ARMA Models
Yeo, In-Kwon ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 71~77
DOI : 10.5351/CKSS.2011.18.1.071
In this paper, asymptotic results are investigated when a parametric transformation is applied to ARMA models. The conditions are determined to ensure the strong consistency and the asymptotic normality of maximum likelihood estimators and the correct coverage probability of the forecast interval obtained by the transformation and backtransformation approach.
Kalman-Filter Estimation and Prediction for a Spatial Time Series Model
Lee, Sung-Duck ; Han, Eun-Hee ; Kim, Duck-Ki ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 79~87
DOI : 10.5351/CKSS.2011.18.1.079
A spatial time series model was used for analyzing the method of spatial time series (not the ARIMA model that is popular for analyzing spatial time series) by using chicken pox data which is a highly contagious disease and grid data due to ARIMA not reflecting the spatial processes. Time series model contains a weighting matrix, because that spatial time series model influences the time variation as well as the spatial location. The weighting matrix reflects that the more geographically contiguous region has the higher spatial dependence. It is hypothesized that the weighting matrix gives neighboring areas the same influence in the study of the spatial time series model. Therefore, we try to present the conclusion with a weighting matrix in a way that gives the same weight to existing neighboring areas in the study of the suitability of the STARMA model, spatial time series model and STBL model, in the comparative study of the predictive power for statistical inference, and the results. Furthermore, through the Kalman-Filter method we try to show the superiority of the Kalman-Filter method through a parameter assumption and the processes of prediction.
A Comparative Study on Discretization Algorithms for Data Mining
Choi, Byong-Su ; Kim, Hyun-Ji ; Cha, Woon-Ock ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 89~102
DOI : 10.5351/CKSS.2011.18.1.089
The discretization process that converts continuous attributes into discrete ones is a preprocessing step in data mining such as classification. Some classification algorithms can handle only discrete attributes. The purpose of discretization is to obtain discretized data without losing the information for the original data and to obtain a high predictive accuracy when discretized data are used in classification. Many discretization algorithms have been developed. This paper presents the results of our comparative study on recently proposed representative discretization algorithms from the view point of splitting versus merging and supervised versus unsupervised. We implemented R codes for discretization algorithms and made them available for public users.
Confidence Intervals and Joint Confidence Regions for the Two-Parameter Exponential Distribution based on Records
Asgharzadeh, A. ; Abdi, M. ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 103~110
DOI : 10.5351/CKSS.2011.18.1.103
Exponential distribution is widely adopted as a lifetime model. Many authors have considered the interval estimation of the parameters of two-parameter exponential distribution based on complete and censored samples. In this paper, we consider the interval estimation of the location and scale parameters and the joint confidence region of the parameters of two-parameter exponential distribution based on upper records. A simulation study is done for the performance of all proposed confidence intervals and regions. We also propose the predictive intervals of the future records. Finally, a numerical example is given to illustrate the proposed methods.
A Generalized Ratio-cum-Product Estimator of Finite Population Mean in Stratified Random Sampling
Tailor, Rajesh ; Sharma, Balkishan ; Kim, Jong-Min ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 111~118
DOI : 10.5351/CKSS.2011.18.1.111
This paper suggests a ratio-cum product estimator of a finite population mean using information on the coefficient of variation and the fcoefficient of kurtosis of auxiliary variate in stratified random sampling. Bias and MSE expressions of the suggested estimator are derived up to the first degree of approximation. The suggested estimator has been compared with the combined ratio estimator and several other estimators considered by Kadilar and Cingi (2003). In addition, an empirical study is also provided in support of theoretical findings.
The Strategies for the Sustainable Management of Insurance Companies
Jung, Se-Chang ; Seon, Hwan-Kyu ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 119~130
DOI : 10.5351/CKSS.2011.18.1.119
This paper measures and analyzes the performance of insurance companies in Korea in respect to sustainable development and suggest strategic implications based on the analysis. The correlation, regression, ANOVA, and t-test are employed. The results of this study are summarized as follows. First, it shows tat social index is important in the life insurance industry; however, the environmental index, is important in the non-life insurance industry. Second, the result gained by regressing the size and financial soundness on the performance of sustainable development demonstrates that the size variable is statistically significant. It suggests that size is a necessary condition for sustainable development. Finally, ANOVA shows that the small and medium sized companies have a significantly poor performance compared to the large companies concerning the social index and reputation index in the life insurance industry. The small and medium sized companies in the non-life insurance industry exhibit a significantly poor performance compared to the large companies in respect to all the indexes, except for the social index. Therefore, the small and medium sized companies make every endeavor in the poor indexes to improve performance.
Accuracy of Multiple Outlier Tests in Nonlinear Regression
Kahng, Myung-Wook ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 131~136
DOI : 10.5351/CKSS.2011.18.1.131
The original Bates-Watts framework applies only to the complete parameter vector. Thus, guidelines developed in that framework can be misleading when the adequacy of the linear approximation is very different for different subsets. The subset curvature measures appear to be reliable indicators of the adequacy of linear approximation for an arbitrary subset of parameters in nonlinear models. Given the specific mean shift outlier model, the standard approaches to obtaining test statistics for outliers are discussed. The accuracy of outlier tests is investigated using subset curvatures.
A Study on the Judgement Rating for Level of Need for Long-term Care Insurance Using a Decision Tree
Han, Sang-Tae ; Kang, Hyun-Cheol ; Choi, Bo-Seung ; Lee, Seong-Keon ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 137~146
DOI : 10.5351/CKSS.2011.18.1.137
Long-term care insurance is a social insurance system that provides benefits to the elderly who have difficulty taking care of themselves for a period of at least 6 months. This system was started in July, 2008 and it is very important to set proper judgement ratings for the approval process. We try to develop and improve the judgement rating system using decision tree models. Our tree model is found to be more stable and efficient than the previous one.
Folded Ranked Set Sampling for Asymmetric Distributions
Bani-Mustafa, Ahmed ; Al-Nasser, Amjad D. ; Aslam, Muhammad ;
Communications for Statistical Applications and Methods, volume 18, issue 1, 2011, Pages 147~153
DOI : 10.5351/CKSS.2011.18.1.147
In this paper a new sampling procedure for estimating the population mean is introduced. The performance of the new population mean estimator is discussed, along with its properties, and it is shown that the proposed method generates an unbiased estimator. The relative efficiency of the suggested estimator is computed, in regards to the simple random sample(SRS), and comparisons are made to the ranked set sampling(RSS) and extreme ranked set sampling(ERSS) estimators used for asymmetric distributions. The results indicate that the proposed estimator is more efficient than the estimators based on the ERSS. In addition, the folded ranked set sampling(FRSS) procedure has an advantage over the RSS and ERSS in that it reduces the number of unused sampling units.