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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 19, Issue 6 - Nov 2012
Volume 19, Issue 5 - Sep 2012
Volume 19, Issue 4 - Jul 2012
Volume 19, Issue 3 - May 2012
Volume 19, Issue 2 - Mar 2012
Volume 19, Issue 1 - Jan 2012
Selecting the target year
Asymptotic Behavior of the Weighted Cross-Variation of a Fractional Brownian Sheet
Kim, Yoon-Tae ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 303~313
DOI : 10.5351/CKSS.2012.19.3.303
By using the techniques of a Malliavin calculus, we study the asymptotic behavior of the weighted cross-variation of a fractional Brownian sheet with a Hurst parameter $H
An Improved Sample Design for Estimating the Usage of Copyrighted Music Works
Lee, Kay-O ; Chung, Yeon-Soo ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 315~331
DOI : 10.5351/CKSS.2012.19.3.315
In this research, we estimated the number of hits per song and its sampling error from 11 (areas including Gangnam) based on log data compiling the number of hits collected from offline karaoke players in March 2011. Then, we calculated the monetary equivalent of the sampling error under the current system that distribute royalties from the karaoke players to copyright holders(song writers and arrangers) according to the estimated hits. Because of the small sample size, the estimated number of hits had a very large sampling error. This research proposes a more reasonable sample design to estimate the usage of copyrighted music works for a fair distribution of royalties by reducing sampling error.
First Order Difference-Based Error Variance Estimator in Nonparametric Regression with a Single Outlier
Park, Chun-Gun ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 333~344
DOI : 10.5351/CKSS.2012.19.3.333
We consider some statistical properties of the first order difference-based error variance estimator in nonparametric regression models with a single outlier. So far under an outlier(s) such difference-based estimators has been rarely discussed. We propose the first order difference-based estimator using the leave-one-out method to detect a single outlier and simulate the outlier detection in a nonparametric regression model with the single outlier. Moreover, the outlier detection works well. The results are promising even in nonparametric regression models with many outliers using some difference based estimators.
An Alternative Parametric Estimation of Sample Selection Model: An Application to Car Ownership and Car Expense
Choi, Phil-Sun ; Min, In-Sik ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 345~358
DOI : 10.5351/CKSS.2012.19.3.345
In a parametric sample selection model, the distribution assumption is critical to obtain consistent estimates. Conventionally, the normality assumption has been adopted for both error terms in selection and main equations of the model. The normality assumption, however, may excessively restrict the true underlying distribution of the model. This study introduces the
-normal distribution into the error distribution of a sample selection model. The
-normal distribution can accommodate a wide range of skewness and kurtosis compared to the normal distribution. It also includes the normal distribution as a limiting distribution. Moreover, the
-normal distribution can be easily extended to multivariate dimensions. We provide the log-likelihood function and expected value formula based on a bivariate
-normal distribution in a sample selection model. The results of simulations indicate the
-normal model outperforms the normal model for the consistency of estimators. As an empirical application, we provide the sample selection model for car ownership and a car expense relationship.
A Headache Diagnosis Method Using an Aggregate Operator
Ahn, Jeong-Yong ; Choi, Kyung-Ho ; Park, Jeong-Hyun ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 359~365
DOI : 10.5351/CKSS.2012.19.3.359
The fuzzy set framework has a number of properties that make it suitable to formulize uncertain information in medical diagnosis. This study introduces a fuzzy diagnostic method based on the interval-valued interview chart and the interval-valued intuitionistic fuzzy weighted arithmetic average(IIFWAA) operator. An issue in the use of the IIFWAA operator is to determine the weights. In this study, we propose the occurrence information of symptoms as the weights. An illustrative example is provided to demonstrate its practicality and effectiveness.
Predicting Korea Pro-Baseball Rankings by Principal Component Regression Analysis
Bae, Jae-Young ; Lee, Jin-Mok ; Lee, Jea-Young ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 367~379
DOI : 10.5351/CKSS.2012.19.3.367
In baseball rankings, prediction has been a subject of interest for baseball fans. To predict these rankings, (based on 2011 data from Korea Professional Baseball records) the arithmetic mean method, the weighted average method, principal component analysis, and principal component regression analysis is presented. By standardizing the arithmetic average, the correlation coefficient using the weighted average method, using principal components analysis to predict rankings, the final model was selected as a principal component regression model. By practicing regression analysis with a reduced variable by principal component analysis, we propose a rank predictability model of a pitcher part, a batter part and a pitcher batter part. We can estimate a 2011 rank of pro-baseball by a predicted regression model. By principal component regression analysis, the pitcher part, the other part, the pitcher and the batter part of the ranking prediction model is proposed. The regression model predicts the rankings for 2012.
Causal Relationship of the Logistic Area for Military Service Satisfaction
Kim, Woo-Hyun ; Choi, Yong-Seok ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 381~393
DOI : 10.5351/CKSS.2012.19.3.381
This study is to understand the logistic area effect for the satisfaction of military service; service people work the unit of Marine Corps which is close or far away from North Korea and infantry people from the structural equation models based on the component based method(PLS). From the result, we note that the trustworthy and suitability of supply are the most important factors in their of military service satisfaction for Marine Corps.
Process Improvement in Feedback Adjustment
Lee, Jae-June ; Kim, Yong-Hee ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 395~403
DOI : 10.5351/CKSS.2012.19.3.395
Process adjustment, also called engineering process control(EPC), is applied to maintain process output close to a target value by manipulating controllable variables, but special causes may still make the process deviate from the target and result in significant costs. Thus, it is important to detect and mediate deviations as early as possible. We propose a one-step detection method, the moving search block(MSB), with which the time and type of a special cause can be identified in short periods. A modified control rule that can entertain the effects of the special cause is proposed. A numerical example is presented to evaluate the performance of the proposed scheme.
On Statistical Inference of Stratified Population Mean with Bootstrap
Heo, Tae-Young ; Lee, Doo-Ri ; Cho, Joong-Jae ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 405~414
DOI : 10.5351/CKSS.2012.19.3.405
In a stratified sample, the sampling frame is divided into non-overlapping groups or strata (e.g. geographical areas, age-groups, and genders). A sample is taken from each stratum, if this sample is a simple random sample it is referred to as stratified random sampling. In this paper, we study the bootstrap inference (including confidence interval) and test for a stratified population mean. We also introduce the bootstrap consistency based on limiting distribution related to the plug-in estimator of the population mean. We suggest three bootstrap confidence intervals such as standard bootstrap method, percentile bootstrap method and studentized bootstrap method. We also suggest a bootstrap test method computing the
(Achieved Significance Level). The results of estimation are verified using simulation.
A Study on Performance Analysis of Short Term Internet Traffic Forecasting Models
Ha, M.H. ; Son, H.G. ; Kim, S. ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 415~422
DOI : 10.5351/CKSS.2012.19.3.415
In this paper, we first the compare the performance of Holt-Winters, FSARIMA, AR-GARCH and Seasonal AR-GARCH models with in the short term based data. The results of the compared data show that the Holt-Winters model outperformed other models in terms of forecasting accuracy.
Multiple Structural Change-Point Estimation in Linear Regression Models
Kim, Jae-Hee ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 423~432
DOI : 10.5351/CKSS.2012.19.3.423
This paper is concerned with the detection of multiple change-points in linear regression models. The proposed procedure relies on the local estimation for global change-point estimation. We propose a multiple change-point estimator based on the local least squares estimators for the regression coefficients and the split measure when the number of change-points is unknown. Its statistical properties are shown and its performance is assessed by simulations and real data applications.
Statistical Modeling of Learning Curves with Binary Response Data
Lee, Seul-Ji ; Park, Man-Sik ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 433~450
DOI : 10.5351/CKSS.2012.19.3.433
As a worker performs a certain operation repeatedly, he tends to become familiar with the job and complete it in a very short time. That means that the efficiency is improved due to his accumulated knowledge, experience and skill in regards to the operation. Investing time in an output is reduced by repeating any operation. This phenomenon is referred to as the learning curve effect. A learning curve is a graphical representation of the changing rate of learning. According to previous literature, learning curve effects are determined by subjective pre-assigned factors. In this study, we propose a new statistical model to clarify the learning curve effect by means of a basic cumulative distribution function. This work mainly focuses on the statistical modeling of binary data. We employ the Newton-Raphson method for the estimation and Delta method for the construction of confidence intervals. We also perform a real data analysis.
Fourier Series Approximation for the Generalized Baumgartner Statistic
Ha, Hyung-Tae ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 451~457
DOI : 10.5351/CKSS.2012.19.3.451
Baumgartner et al. (1998) proposed a novel statistical test for the null hypothesis that two independently drawn samples of data originate from the same population, and Murakami (2006) generalized the test statistic for more than two samples. Whereas the expressions of the exact density and distribution functions of the generalized Baumgartner statistic are not yet found, the characteristic function of its limiting distribution has been obtained. Due to the development of computational power, the Fourier series approximation can be readily utilized to accurately and efficiently approximate its density function based on its Laplace transform. Numerical examples show that the Fourier series method provides an accurate approximation for statistical quantities of the generalized Baumgartner statistic.
Parallel Implementations of the Self-Organizing Network for Normal Mixtures
Lee, Chul-Hee ; Ahn, Sung-Mahn ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 459~469
DOI : 10.5351/CKSS.2012.19.3.459
This article proposes a couple of parallel implementations of the self-organizing network for normal mixtures. In principle, self-organizing networks should be able to be implemented in a parallel computing environment without issue. However, the network for normal mixtures has inherent problem in being operated parallel in pure sense due to estimating conditional expectations of the mixing proportion in each iteration. This article shows the result of the parallel implementations of the network using Java. According to the results, both of the implementations achieved a faster execution without any performance degradation.
Canonical Correlation: Permutation Tests and Regression
Yoo, Jae-Keun ; Kim, Hee-Youn ; Um, Hye-Yeon ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 471~478
DOI : 10.5351/CKSS.2012.19.3.471
In this paper, we present a permutation test to select the number of pairs of canonical variates in canonical correlation analysis. The existing chi-squared test is known to be limited to normality in use. We compare the existing test with the proposed permutation test and study their asymptotic behaviors through numerical studies. In addition, we connect canonical correlation analysis to regression and we we show that certain inferences in regression can be done through canonical correlation analysis. A regression analysis of real data through canonical correlation analysis is illustrated.
Bayesian Estimators Using Record Statistics of Exponentiated Inverse Weibull Distribution
Kim, Yong-Ku ; Seo, Jung-In ; Kang, Suk-Bok ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 479~493
DOI : 10.5351/CKSS.2012.19.3.479
The inverse Weibull distribution(IWD) is a complementary Weibull distribution and plays an important role in many application areas. In this paper, we develop a Bayesian estimator in the context of record statistics values from the exponentiated inverse Weibull distribution(EIWD). We obtained Bayesian estimators through the squared error loss function (quadratic loss) and LINEX loss function. This is done with respect to the conjugate priors for shape and scale parameters. The results may be of interest especially when only record values are stored.
Bayesian Test of Quasi-Independence in a Sparse Two-Way Contingency Table
Kwak, Sang-Gyu ; Kim, Dal-Ho ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 495~500
DOI : 10.5351/CKSS.2012.19.3.495
We consider a Bayesian test of independence in a two-way contingency table that has some zero cells. To do this, we take a three-stage hierarchical Bayesian model under each hypothesis. For prior, we use Dirichlet density to model the marginal cell and each cell probabilities. Our method does not require complicated computation such as a Metropolis-Hastings algorithm to draw samples from each posterior density of parameters. We draw samples using a Gibbs sampler with a grid method. For complicated posterior formulas, we apply the Monte-Carlo integration and the sampling important resampling algorithm. We compare the values of the Bayes factor with the results of a chi-square test and the likelihood ratio test.
Investigating SIR, DOC and SAVE for the Polychotomous Response
Lee, Hak-Bae ; Lee, Hee-Min ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 501~506
DOI : 10.5351/CKSS.2012.19.3.501
This paper investigates the central subspace related with SIR, DOC and SAVE when the response has more than two values. The subspaces constructed by SIR, DOC and SAVE are investigated and compared. The SAVE paradigm is the most comprehensive. In addition, the SAVE coincides with the central subspace when the conditional distribution of predictors given the response is normally distributed.
Estimation of Layered Periodic Autoregressive Moving Average Models
Lee, Sung-Duck ; Kim, Jung-Gun ; Kim, Sun-Woo ;
Communications for Statistical Applications and Methods, volume 19, issue 3, 2012, Pages 507~516
DOI : 10.5351/CKSS.2012.19.3.507
We study time series models for seasonal time series data with a covariance structure that depends on time and the periodic autocorrelation at various lags
. In this paper, we introduce an ARMA model with periodically varying coefficients(PARMA) and analyze Arosa ozone data with a periodic correlation in the practical case study. Finally, we use a PARMA model and a seasonal ARIMA model for data analysis and show the performance of a PARMA model with a comparison to the SARIMA model.