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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 23, Issue 6 - Dec 2010
Volume 23, Issue 5 - Oct 2010
Volume 23, Issue 4 - Aug 2010
Volume 23, Issue 3 - Jun 2010
Volume 23, Issue 2 - Apr 2010
Volume 23, Issue 1 - Feb 2010
Selecting the target year
Estimation of VaR Using Extreme Losses, and Back-Testing: Case Study
Seo, Sung-Hyo ; Kim, Sung-Gon ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 219~234
DOI : 10.5351/KJAS.2010.23.2.219
In index investing according to KOSPI, we estimate Value at Risk(VaR) from the extreme losses of the daily returns which are obtained from KOSPI. To this end, we apply Block Maxima(BM) model which is one of the useful models in the extreme value theory. We also estimate the extremal index to consider the dependency in the occurrence of extreme losses. From the back-testing based on the failure rate method, we can see that the model is adaptable for the VaR estimation. We also compare this model with the GARCH model which is commonly used for the VaR estimation. Back-testing says that there is no meaningful difference between the two models if we assume that the conditional returns follow the t-distribution. However, the estimated VaR based on GARCH model is sensitive to the extreme losses occurred near the epoch of estimation, while that on BM model is not. Thus, estimating the VaR based on GARCH model is preferred for the short-term prediction. However, for the long-term prediction, BM model is better.
Classification and Comparison of the Type of Graduates Job Mobility
Chun, Young-Min ; Lee, Seong-Jae ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 235~247
DOI : 10.5351/KJAS.2010.23.2.235
In this paper it is investigated how the number of work experiences is distributed among college graduates who have ever entered the labor market and built up career by turnover. To do so, we classified the type of work experience and, moreover, conduct ANOVA to explore wage differentials caused by the number of work experience and by the type of work experience, using the GOMS(graduates occupational mobility survey) from 2006 to 2007.
Long-Term Forecasting by Wavelet-Based Filter Bank Selections and Its Application
Lee, Jeong-Ran ; Lee, You-Lim ; Oh, Hee-Seok ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 249~261
DOI : 10.5351/KJAS.2010.23.2.249
Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.
Statistical Properties of Business Survey Index
Kim, Kyu-Seong ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 263~274
DOI : 10.5351/KJAS.2010.23.2.263
Business survey index(BSI) is an economic forecasting index made on the basis of the past achievement of the company and enterpriser's plan and decision for the future. Even the index is very popular in economic situations, only a little research result is known to the public. In the paper we investigate statistical properties of BSI. We define population BSI in the finite population and estimate it unbiasedly. Also we derive the variance of the estimated BSI and its unbiased estimator. In addition, confidence interval of the estimated BSI is proposed. We asserte that confidence interval of the estimated BSI is more reasonable than the relative standard error.
A Study for Shapes of Filter on the Prior Adjustment of the Holiday Effect
Kim, Kee-Whan ; Shin, Hyun-Gyu ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 275~284
DOI : 10.5351/KJAS.2010.23.2.275
In this study, we introduce filters that used for the prior adjustment of the holiday effect in seasonal adjustment. And we propose new filters having more various and flexible patterns than conventional ones. Under the practical assumption that patterns of effects before and after the holiday are different, we compare adjustment effect of the proposed filters and the existing ones. In comparison study, we estimate the effect from all possible combinations of shapes of filter by RegARIMA. And then, to adjust holiday effect, we apply the estimated results to time series data of industrial production and shipment index data in South Korea.
Animated Quantile Plots for Evaluating Response Surface Designs
Jang, Dae-Heung ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 285~293
DOI : 10.5351/KJAS.2010.23.2.285
The traditional methods for evaluating response surface designs are alphabetic optimality criteria. These single-number criteria such as D-, A-, G- and V-optimality do not completely reflect the prediction variance characteristics of the design in question. Alternatives to single-numbers summaries include graphical displays of the prediction variance across the design regions. We can suggest the animated quantile plots as the animation of the quantile plots and use these animated quantile plots for comparing and evaluating response surface designs.
A Prediction Model for Coating Thickness Based on PLS Model and Variable Selection
Lee, Hye-Seon ; Lee, Young-Rok ; Jun, Chi-Hyuck ; Hong, Jae-Hwa ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 295~304
DOI : 10.5351/KJAS.2010.23.2.295
Coating thickness is one of target variables in quality control process in steel industry. To predict coating thickness and to control quality of anti-fingerprint steel coils, ultraviolet-visible spectra are measured. We propose a variable-interval selection procedure based on the variable importance in projection in partial least square model. Using the proposed variable interval selection method, prediction performance gets better in the reduced model than the full model with full spectra absorbance. It is also shown that the first differencing as a data preprocessing technique does work well for the prediction of coating thickness.
An EM Algorithm-Based Approach for Imputation of Pixel Values in Color Image
Kim, Seung-Gu ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 305~315
DOI : 10.5351/KJAS.2010.23.2.305
In this paper, a frequentistic approach to impute the values of R, G, B-components in random missing pixels of color image is provided. Under assumption that the given image is a realization of Gaussian Markov random field, its model is designed such that each neighbor pixel values for a given pixel follows (independently) the normal distribution with covariance matrix scaled by an evaluates of the similarity between two pixel values, so that the imputation is not to be affected by the neighbors with different color. An approximate EM-based algorithm maximizing the underlying likelihood is implemented to estimate the parameters and to impute the missing pixel values. Some experiments are presented to show its effectiveness through performance comparison with a popular interpolation method.
Unbalanced ANOVA for Testing Shape Variability in Statistical Shape Analysis
Kim, Jong-Geon ; Choi, Yong-Seok ; Lee, Nae-Young ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 317~323
DOI : 10.5351/KJAS.2010.23.2.317
Measures are very useful tools for comparing the shape variability in statistical shape analysis. For examples, the Procrustes statistic(PS) is isolated measure, and the mean Procrustes statistic(MPS) and the root mean square measure(RMS) are overall measures. But these measures are very subjective, complicated and moreover these measures are not statistical for comparing the shape variability. Therefore we need to study some tests. It is well known that the Hotelling's
test is used for testing shape variability of two independent samples. And for testing shape variabilities of several independent samples, instead of the Hotelling's
test, one way analysis of variance(ANOVA) can be applied. In fact, this one way ANOVA is based on the balanced samples of equal size which is called as BANOVA. However, If we have unbalanced samples with unequal size, we can not use BANOVA. Therefore we propose the unbalanced analysis of variance(UNBANOVA) for testing shape variabilities of several independent samples of unequal size.
Investigations into Coarsening Continuous Variables
Jeong, Dong-Myeong ; Kim, Jay-J. ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 325~333
DOI : 10.5351/KJAS.2010.23.2.325
Protection against disclosure of survey respondents' identifiable and/or sensitive information is a prerequisite for statistical agencies that release microdata files from their sample surveys. Coarsening is one of popular methods for protecting the confidentiality of the data. Grouped data can be released in the form of microdata or tabular data. Instead of releasing the data in a tabular form only, having microdata available to the public with interval codes with their representative values greatly enhances the utility of the data. It allows the researchers to compute covariance between the variables and build statistical models or to run a variety of statistical tests on the data. It may be conjectured that the variance of the interval data is lower that of the ungrouped data in the sense that the coarsened data do not have the within interval variance. This conjecture will be investigated using the uniform and triangular distributions. Traditionally, midpoint is used to represent all the values in an interval. This approach implicitly assumes that the data is uniformly distributed within each interval. However, this assumption may not hold, especially in the last interval of the economic data. In this paper, we will use three distributional assumptions - uniform, Pareto and lognormal distribution - in the last interval and use either midpoint or median for other intervals for wage and food costs of the Statistics Korea's 2006 Household Income and Expenditure Survey(HIES) data and compare these approaches in terms of the first two moments.
Analysis of Total Crime Count Data Based on Spatial Association Structure
Choi, Jung-Soon ; Park, Man-Sik ; Won, Yu-Bok ; Kim, Hag-Yeol ; Heo, Tae-Young ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 335~344
DOI : 10.5351/KJAS.2010.23.2.335
Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.
Spatial Prediction of Wind Speed Data
Jeong, Seung-Hwan ; Park, Man-Sik ; Kim, Kee-Whan ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 345~356
DOI : 10.5351/KJAS.2010.23.2.345
In this paper, we introduce the linear regression model taking the parametric spatial association structure into account and employ it to five-year averaged wind speed data measured at 460 meteorological monitoring stations in South Korea. From the prediction map obtained by the model with spatial association parameters, we can see that inland area has smaller wind speed than coastal regions. When comparing the spatial linear regression model with classical one by using one-leave-out cross-validation, the former outperforms the latter in terms of similarity between the observations and the corresponding predictions and coverage rate of 95% prediction intervals.
Variable Selection with Regression Trees
Chang, Young-Jae ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 357~366
DOI : 10.5351/KJAS.2010.23.2.357
Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many noise variables. To handle this problem, we propose the multi-step GUIDE, which is a regression tree algorithm with a variable selection process. The multi-step GUIDE performs better than some of the well-known algorithms such as Random Forest and MARS. The results based on simulation study shows that the multi-step GUIDE outperforms other algorithms in terms of variable selection and prediction accuracy. It generally selects the important variables correctly with relatively few noise variables and eventually gives good prediction accuracy.
Sample Based Algorithm for k-Spatial Medians Clustering
Jin, Seo-Hoon ; Jung, Byoung-Cheol ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 367~374
DOI : 10.5351/KJAS.2010.23.2.367
As an alternative to the k-means clustering the k-spatial medians clustering has many good points because of advantages of spatial median. However, it has not been used a lot since it needs heavy computation. If the number of objects and the number of variables are large the computation time problem is getting serious. In this study we propose fast algorithm for the k-spatial medians clustering. Practical applicability of the algorithm is shown with some numerical studies.
A Trimmed Spatial Median Estimator Using Bootstrap Method
Lee, Dong-Hee ; Jung, Byoung-Cheol ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 375~382
DOI : 10.5351/KJAS.2010.23.2.375
In this study, we propose a robust estimator of the multivariate location parameter by means of the spatial median based on data trimming which extending trimmed mean in the univariate setup. The trimming quantity of this estimator is determined by the bootstrap method, and its covariance matrix is estimated by using the double bootstrap method. This extends the work of Jhun et al. (1993) to the multivariate case. Monte Carlo study shows that the proposed trimmed spatial median estimator yields better efficiency than a spatial median, while its covariance matrix based on double bootstrap overcomes the under-estimating problem occurred on single bootstrap method.
A Note on Series Approximation of Transition Density of Diffusion Processes
Lee, Eun-Kyung ; Choi, Young-Soo ; Lee, Yoon-Dong ;
Korean Journal of Applied Statistics, volume 23, issue 2, 2010, Pages 383~392
DOI : 10.5351/KJAS.2010.23.2.383
Modelling financial phenomena with diffusion processes is frequently used technique. This study reviews the earlier researches on the approximation problem of transition densities of diffusion processes, which takes important roles in estimating diffusion processes, and consider the method to obtain the coefficients of series efficiently, in series approximation method of transition densities. We developed a new efficient algorithm to compute the coefficients which are represented by repeated Dynkin operator on Hermite polynomial.