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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 28, Issue 6 - Dec 2015
Volume 28, Issue 5 - Oct 2015
Volume 28, Issue 4 - Aug 2015
Volume 28, Issue 3 - Jun 2015
Volume 28, Issue 2 - Apr 2015
Volume 28, Issue 1 - Feb 2015
Selecting the target year
A Study on Sample Allocation for Stratified Sampling
Lee, Ingue ; Park, Mingue ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1047~1061
DOI : 10.5351/KJAS.2015.28.6.1047
Stratified random sampling is a powerful sampling strategy to reduce variance of the estimators by incorporating useful auxiliary information to stratify the population. Sample allocation is the one of the important decisions in selecting a stratified random sample. There are two common methods, the proportional allocation and Neyman allocation if we could assume data collection cost for different observation units equal. Theoretically, Neyman allocation considering the size and standard deviation of each stratum, is known to be more effective than proportional allocation which incorporates only stratum size information. However, if the information on the standard deviation is inaccurate, the performance of Neyman allocation is in doubt. It has been pointed out that Neyman allocation is not suitable for multi-purpose sample survey that requires the estimation of several characteristics. In addition to sampling error, non-response error is another factor to evaluate sampling strategy that affects the statistical precision of the estimator. We propose new sample allocation methods using the available information about stratum response rates at the designing stage to improve stratified random sampling. The proposed methods are efficient when response rates differ considerably among strata. In particular, the method using population sizes and response rates improves the Neyman allocation in multi-purpose sample survey.
A Target Selection Model for the Counseling Services in Long-Term Care Insurance
Han, Eun-Jeong ; Kim, Dong-Geon ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1063~1073
DOI : 10.5351/KJAS.2015.28.6.1063
In the long-term care insurance (LTCI) system, National Health Insurance Service (NHIS) provide counseling services for beneficiaries and their family caregivers, which help them use LTC services appropriately. The purpose of this study was to develop a Target Selection Model for the Counseling Services based on needs of beneficiaries and their family caregivers. To develope models, we used data set of total 2,000 beneficiaries and family caregivers who have used the long-term care services in their home in March 2013 and completed questionnaires. The Target Selection Model was established through various data-mining models such as logistic regression, gradient boosting, Lasso, decision-tree model, Ensemble, and Neural network. Lasso model was selected as the final model because of the stability, high performance and availability. Our results might improve the satisfaction and the efficiency for the NHIS counseling services.
Economic Phenomena, Economic Analysis, and Its Statistical Applicability: Focusing on the Developments of Econometrics and Challenging Issues
Kim, Chiho ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1075~1091
DOI : 10.5351/KJAS.2015.28.6.1075
This paper reviews the developments of econometric analysis and seeks a statistical applicability to current economic phenomena. During the last half century, economic analysis has progressed continuously, analyzing and predicting a broad variety of economic phenomena. In the center of this progress lies the remarkable contribution of econometrics and mathematical statistics. New economic research environment has been recently created via developments of IT and the spread of internet and SNSs. Economic phenomena has become increasingly complicated along with more volatile and sophisticated economic analysis. In that context, it can be suggested that there is a need to move beyond current economic paradigms and adapt new approaches such as complex theory and econophysics, all of which posits as a challenge for econometrics and statistics.
Variance Components of Nested Designs
Choi, Jaesung ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1093~1101
DOI : 10.5351/KJAS.2015.28.6.1093
This paper discusses nested design models when nesting occurs in treatment structure and design structure. Some are fixed and others are random; subsequently, the fixed factors having a nested design structure are assumed to be nested in the random factors. The treatment structure can involve random and fixed effects as well as a design structure that can involve several sizes of experimental units. This shows how to use projections for sums of squares by fitting the model in a stepwise procedure. Expectations of sums of squares are obtained via synthesis. Variance components of the nested design model are estimated by the method of moments.
Relative Error Prediction via Penalized Regression
Jeong, Seok-Oh ; Lee, Seo-Eun ; Shin, Key-Il ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1103~1111
DOI : 10.5351/KJAS.2015.28.6.1103
This paper presents a new prediction method based on relative error incorporated with a penalized regression. The proposed method consists of fully data-driven procedures that is fast, simple, and easy to implement. An example of real data analysis and some simulation results were given to prove that the proposed approach works in practice.
A Stratified Multi-proportions Randomized Response Model
Lee, Gi-Sung ; Park, Kyung-Soon ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1113~1120
DOI : 10.5351/KJAS.2015.28.6.1113
We propose a multi-proportions randomized response model by stratified simple random sampling for surveys of sensitive issues of a polychotomous population composed of several stratum. We also systemize a theoretical validity to apply multi-proportions randomized response model (Abul-Ela et al.' model, Eriksson's model) to stratified simple random sampling and derive the estimate and its dispersion matrix of the proportion of sensitive characteristic of population using the suggested model. Two types of sample allocations (proportional allocation and optimum allocation) are considered under the fixed cost. In efficiency, the Eriksson's model by stratified sampling are compared to the Abul-Ela et al.' model.
Semiparametric Approach to Logistic Model with Random Intercept
Kim, Mijeong ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1121~1131
DOI : 10.5351/KJAS.2015.28.6.1121
Logistic models with a random intercept are useful to analyze longitudinal binary data. Traditionally, the random intercept of the logistic model is assumed to be parametric (such as normal distribution) and is also assumed to be independent to variables. Such assumptions are very strong and restricted for application to real data. Recently, Garcia and Ma (2015) derived semiparametric efficient estimators for logistic model with a random intercept without these assumptions. Their estimator shows the consistency where we do not assume any parametric form for the random intercept. In addition, the method is computationally simple. In this paper, we apply this method to analyze toenail infection data. We compare the semiparametric estimator with maximum likelihood estimator, penalized quasi-likelihood estimator and hierarchical generalized linear estimator.
Analysis of Horse Races: Prediction of Winning Horses in Horse Races Using Statistical Models
Choe, Hyemin ; Hwang, Nayoung ; Hwang, Chankyoung ; Song, Jongwoo ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1133~1146
DOI : 10.5351/KJAS.2015.28.6.1133
The Horse race industry has the largest proportion of the domestic legal gambling industry. However, there is limited statistical analysis on horse races versus other sports. We propose prediction models for winning horses in horse races using data mining techniques such as logistic regression, linear regression, and random forest. Horse races data are from the Korea Racing Authority and we use horse racing reports, information of racehorses, jockeys, and horse trainers. We consider two models based on ranks and time records. The analysis results show that prediction of ranks is affected by information on racehorses, number of wins of racehorses and jockeys. We place wagers for the last month of races based on our prediction models that produce serious profits.
A Divisive Clustering for Mixed Feature-Type Symbolic Data
Kim, Jaejik ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1147~1161
DOI : 10.5351/KJAS.2015.28.6.1147
Nowadays we are considering and analyzing not only classical data expressed by points in the p-dimensional Euclidean space but also new types of data such as signals, functions, images, and shapes, etc. Symbolic data also can be considered as one of those new types of data. Symbolic data can have various formats such as intervals, histograms, lists, tables, distributions, models, and the like. Up to date, symbolic data studies have mainly focused on individual formats of symbolic data. In this study, it is extended into datasets with both histogram and multimodal-valued data and a divisive clustering method for the mixed feature-type symbolic data is introduced and it is applied to the analysis of industrial accident data.
Volatility Computations for Financial Time Series: High Frequency and Hybrid Method
Yoon, J.E. ; Hwang, S.Y. ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1163~1170
DOI : 10.5351/KJAS.2015.28.6.1163
Various computational methods for obtaining volatilities for financial time series are reviewed and compared with each other. We reviewed model based GARCH approach as well as the data based method which can essentially be regarded as a smoothing technique applied to the squared data. The method for high frequency data is focused to obtain the realized volatility. A hybrid method is suggested by combining the model based GARCH and the historical volatility which is a data based method. Korea stock prices are analysed to illustrate various computational methods for volatilities.
Multidimensional Scaling Using the Pseudo-Points Based on Partition Method
Shin, Sang Min ; Kim, Eun-Seong ; Choi, Yong-Seok ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1171~1180
DOI : 10.5351/KJAS.2015.28.6.1171
Multidimensional scaling (MDS) is a graphical technique of multivariate analysis to display dissimilarities among individuals into low-dimensional space. We often have two kinds of MDS which are metric MDS and non-metric MDS. Metric MDS can be applied to quantitative data; however, we need additional information about variables because it only shows relationships among individuals. Gower (1992) proposed a method that can represent variable information using trajectories of the pseudo-points for quantitative variables on the metric MDS space. We will call his method a 'replacement method'. However, the trajectory can not be represented even though metric MDS can be applied to binary data when we apply his method to binary data. Therefore, we propose a method to represent information of binary variables using pseudo-points called a 'partition method'. The proposed method partitions pseudo-points, accounting both the rate of zeroes and ones. Our metric MDS using the proposed partition method can show the relationship between individuals and variables for binary data.
Nonparametric Method in One-way Layout for Umbrella Alternatives based on Placement
Lee, Hyejung ; Kim, Dongjae ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1181~1189
DOI : 10.5351/KJAS.2015.28.6.1181
The treatment effect in clinical tests depending on dose of the drug; however, it can show a decreasing trend in fixed dose level due to side effects. The trend is known as an umbrella pattern; in addition, the method for the umbrella alternative is quite useful when the tendency is predicted in advance. In this paper, we propose a nonparametric method of umbrella alternatives for a one-way layout by using linear placement described in Orban and Wolfe (1982). The Monte Carlo simulation is adapted to compare the power of proposed procedure with previous methods.
Various Graphical Methods for Assessing a Logistic Regression Model
Kim, Kyung Jin ; Kahng, Myung Wook ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1191~1208
DOI : 10.5351/KJAS.2015.28.6.1191
Most statistical methods are dependent on the summary statistic. However, with graphical approaches, it is easier to identify the characteristics of the data and detect information that cannot be obtained by the summary statistic. We present various graphical methods to assess the adequacy of models in logistic regression that include checking log-density ratio, structural dimension, marginal model plot, chi-residual plot, and CERES plot. Through simulation data, we investigate and compare the results of graphical approaches under diverse conditions.
A Joint Frailty Model for Competing Risks Survival Data
Ha, Il Do ; Cho, Geon-Ho ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1209~1216
DOI : 10.5351/KJAS.2015.28.6.1209
Competing-risks events are often observed in a clustered clinical study such as a multi-center clinical trial. We propose a joint modelling approach via a shared frailty term for competing risks survival data from a cluster. For the inference we use the hierarchical likelihood (or h-likelihood), which avoids an intractable integration. We derive the corresponding h-likelihood procedure. The proposed method is illustrated via the analysis of a practical data set.
Understanding Complex Design Features via Design Effect Models
Park, Inho ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1217~1225
DOI : 10.5351/KJAS.2015.28.6.1217
Survey research, data is commonly collected through a sample design with complex design features that allow the relative efficiency on the precision of an estimator to be measured using the concept of the design effect compared to simple random sampling as a reference design. This concept is most useful when the design effect can be expressed as a function of various design features. We propose a design effect formula suitable under a stratified multistage sampling by generalizing Gabler et al. (1999, 2006)'s approaches for multistage sampling. Its use can either guide improvement in the design efficiency when in design stage or enable the evaluation of the adopted design features afterwards.
Minimum Bias Design for Polynomial Regression
Jang, Dae-Heung ; Kim, Youngil ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1227~1234
DOI : 10.5351/KJAS.2015.28.6.1227
Traditional criteria for optimum experimental designs depend on the specifications of the model; however, there will be a dilemma when we do not have perfect knowledge about the model. Box and Draper (1959) suggested one direction to minimize bias that may occur in this situation. We will demonstrate some examples with exact solutions that provide a no-bias design for polynomial regression. The most interesting finding is that a design that requires less bias should allocate design points away from the border of the design space.
A Study on Internet Traffic Forecasting by Combined Forecasts
Kim, Sahm ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1235~1243
DOI : 10.5351/KJAS.2015.28.6.1235
Increased data volume in the ICT area has increased the importance of forecasting accuracy for internet traffic. Forecasting results may have paper plans for traffic management and control. In this paper, we propose combined forecasts based on several time series models such as Seasonal ARIMA and Taylor's adjusted Holt-Winters and Fractional ARIMA(FARIMA). In combined forecasting methods, we use simple-combined method, MSE based method (Armstrong, 2001), Ordinary Least Squares (OLS) method and Equality Restricted Least Squares (ERLS) method. The results show that the Seasonal ARIMA model outperforms in 3 hours ahead forecasts and that combined forecasts outperform in longer periods.
Goodness of Fit and Independence Tests for Major 8 Companies of Korean Stock Market
Min, Seungsik ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1245~1255
DOI : 10.5351/KJAS.2015.28.6.1245
In this paper, we investigated the major 8 companies of Korean stock market, and carried out the goodness of fit and independence tests. We found out the distributions of absolute returns are closed to compressed exponential distribution. The parameters are dominant that 1 <
< 2, followed by
(exponential distribution) and
(normal distribution). Meanwhile, we assured that most of the absolute returns for major 8 companies have relevance to each other by chi-square independence test.
Case Study of Six Sigma Method to Develop Embedded Software in Mobile Phones
Ko, Seoung-Gon ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1257~1273
DOI : 10.5351/KJAS.2015.28.6.1257
The development process of Embedded Software (SW) is gathering interest due to the increased importance of SW in mobile products. According to tough competition and the growing size of the Embedded SW, there is a demand for a new effective way to improve the SW development process, based on customer and market quality aspects, rather than focusing on defect removals in individual SW modules. We review 103 SW improvement projects from the area of mobile phones in order to check the effectiveness of Six Sigma which is the standard for the process improvement statistical tools and methods.
An Object-Based Verification Method for Microscale Weather Analysis Module: Application to a Wind Speed Forecasting Model for the Korean Peninsula
Kim, Hea-Jung ; Kwak, Hwa-Ryun ; Kim, Sang-il ; Choi, Young-Jean ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1275~1288
DOI : 10.5351/KJAS.2015.28.6.1275
A microscale weather analysis module (about 1km or less) is a microscale numerical weather prediction model designed for operational forecasting and atmospheric research needs such as radiant energy, thermal energy, and humidity. The accuracy of the module is directly related to the usefulness and quality of real-time microscale weather information service in the metropolitan area. This paper suggests an object based verification method useful for spatio-temporal evaluation of the accuracy of the microscale weather analysis module. The method is a graphical method comprised of three steps that constructs a lattice field of evaluation statistics, merges and identifies objects, and evaluates the accuracy of the module. We develop lattice fields using various evaluation spatio-temporal statistics as well as an efficient object identification algorithm that conducts convolution, masking, and merging operations to the lattice fields. A real data application demonstrates the utility of the verification method.
A Study on the Comovement of Industry Default
Jeon, Haehyun ; Kim, So-Yeun ; Kim, Changki ;
Korean Journal of Applied Statistics, volume 28, issue 6, 2015, Pages 1289~1312
DOI : 10.5351/KJAS.2015.28.6.1289
This paper studies the comovement of industry defaults among listed companies. Rank correlation coefficients of Spearman's
measure the concordance of default. These non-parametric coefficients do not require distributional assumptions and are easily used even with less data and extreme values. This study predicts a future financial crisis by looking at the comovement of industry defaults. We expect our analyses will aid market participants (including company executives) in making investment or risk management decisions.