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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 29, Issue 5 - Aug 2016
Volume 29, Issue 4 - Jun 2016
Volume 29, Issue 3 - Apr 2016
Volume 29, Issue 2 - Feb 2016
Volume 29, Issue 1 - Feb 2016
Selecting the target year
Statistical testings for common stochastic trends in markets under recession
Cho, Joong-Jae ; Lee, Seung-Eun ; Kim, Tae-Ho ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 559~569
DOI : 10.5351/KJAS.2016.29.4.559
A long-run relationship of stock, monetary, realty markets, and business conditions has been suggested to exist due to internal and external shocks. This study investigates whether such a relationship really exists and then performs statistical tests to discern features of the long-run adjustment processes from short-run discrepancies because it is difficult to find studies that examine the market relationship. The comovement relationship of the whole market does not appear to hold for the entire study period; however, it is found to exist for the period before the financial crisis. Estimated error correction models show consistently declining equilibrium errors each period that suggests a recovering process of the long-run equilibrium from short-run secessions.
Saddlepoint approximation to the distribution function of quadratic forms based on multivariate skew-normal distribution
Na, Jonghwa ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 571~579
DOI : 10.5351/KJAS.2016.29.4.571
Most of studies related to the distributions of quadratic forms are conducted under the assumption of multivariate normal distribution. In this paper, we suggested an approximation to the distribution of quadratic forms based on multivariate skew-normal distribution as alternatives for multivariate normal distribution. Saddlepoint approximations are considered and the accuracy of the approximations are verified through simulation studies.
Hourly electricity demand forecasting based on innovations state space exponential smoothing models
Won, Dayoung ; Seong, Byeongchan ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 581~594
DOI : 10.5351/KJAS.2016.29.4.581
We introduce innovations state space exponential smoothing models (ISS-ESM) that can analyze time series with multiple seasonal patterns. Especially, in order to control complex structure existing in the multiple patterns, the model equations use a matrix consisting of seasonal updating parameters. It enables us to group the seasonal parameters according to their similarity. Because of the grouped parameters, we can accomplish the principle of parsimony. Further, the ISS-ESM can potentially accommodate any number of multiple seasonal patterns. The models are applied to predict electricity demand in Korea that is observed on hourly basis, and we compare their performance with that of the traditional exponential smoothing methods. It is observed that the ISS-ESM are superior to the traditional methods in terms of the prediction and the interpretability of seasonal patterns.
Preliminary test estimation method accounting for error variance structure in nonlinear regression models
Yu, Hyewon ; Lim, Changwon ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 595~611
DOI : 10.5351/KJAS.2016.29.4.595
We use nonlinear regression models (such as the Hill Model) when we analyze data in toxicology and/or pharmacology. In nonlinear regression models an estimator of parameters and estimation of measurement about uncertainty of the estimator are influenced by the variance structure of the error. Thus, estimation methods should be different depending on whether the data are homoscedastic or heteroscedastic. However, we do not know the variance structure of the error until we actually analyze the data. Therefore, developing estimation methods robust to the variance structure of the error is an important problem. In this paper we propose a method to estimate parameters in nonlinear regression models based on a preliminary test. We define an estimator which uses either the ordinary least square estimation method or the iterative weighted least square estimation method according to the results of a simple preliminary test for the equality of the error variance. The performance of the proposed estimator is compared to those of existing estimators by simulation studies. We also compare estimation methods using real data obtained from the National Toxicology program of the United States.
Patent citation network analysis
Lee, Minjung ; Kim, Yongdai ; Jang, Woncheol ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 613~625
DOI : 10.5351/KJAS.2016.29.4.613
The development of technology has changed the world drastically. Patent data analysis helps to understand modern technology trends and predict prospective future technology. In this paper, we analyze the patent citation network using the USPTO data between 1985 and 2012 to identify technology trends. We use network centrality measures that include a PageRank algorithm to find core technologies and identify groups of technology with similar properties with statistical network models.
A nonparametric Bayesian seemingly unrelated regression model
Jo, Seongil ; Seok, Inhae ; Choi, Taeryon ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 627~641
DOI : 10.5351/KJAS.2016.29.4.627
In this paper, we consider a seemingly unrelated regression (SUR) model and propose a nonparametric Bayesian approach to SUR with a Dirichlet process mixture of normals for modeling an unknown error distribution. Posterior distributions are derived based on the proposed model, and the posterior inference is performed via Markov chain Monte Carlo methods based on the collapsed Gibbs sampler of a Dirichlet process mixture model. We present a simulation study to assess the performance of the model. We also apply the model to precipitation data over South Korea.
A study on bias effect of LASSO regression for model selection criteria
Yu, Donghyeon ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 643~656
DOI : 10.5351/KJAS.2016.29.4.643
High dimensional data are frequently encountered in various fields where the number of variables is greater than the number of samples. It is usually necessary to select variables to estimate regression coefficients and avoid overfitting in high dimensional data. A penalized regression model simultaneously obtains variable selection and estimation of coefficients which makes them frequently used for high dimensional data. However, the penalized regression model also needs to select the optimal model by choosing a tuning parameter based on the model selection criterion. This study deals with the bias effect of LASSO regression for model selection criteria. We numerically describes the bias effect to the model selection criteria and apply the proposed correction to the identification of biomarkers for lung cancer based on gene expression data.
Effect of online word-of-mouth variables as predictors of box office
Jeon, Seonghyeon ; Son, Young Sook ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 657~678
DOI : 10.5351/KJAS.2016.29.4.657
This study deals with the effect of online word-of-mouth (OWOM) variables on the box office. From the result of statistical analysis on 276 films with audiences of more than five hundred thousand released in the Korea from 2012 to 2015, it can be seen that the variables showing the size of OWOM (such as the number of the portal movie rater, blog, and news after release) are associated more with the box office than the portal movie rating showing the direction of OWOM as well as variables showing the inherent properties of the film such as grade, nationality, release month, release season, directors, actors, and distributors.
Multiclass loss systems with several server allocation methods
Na, Seongryong ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 679~688
DOI : 10.5351/KJAS.2016.29.4.679
In this paper, we study multiclass loss systems with different server allocation methods. The Markovian states of the systems are defined and their effective representation is investigated. The limiting probabilities are derived based on the Markovian property to determine the performance measures of the systems. The effects of the assignment methods are compared using numerical solutions.
Vector at Risk and alternative Value at Risk
Honga, C.S. ; Han, S.J. ; Lee, G.P. ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 689~697
DOI : 10.5351/KJAS.2016.29.4.689
The most useful method for financial market risk management may be Value at Risk (VaR) which estimates the maximum loss amount statistically. The VaR is used as a risk measure for one industry. Many real cases estimate VaRs for many industries or nationwide industries; consequently, it is necessary to estimate the VaR for multivariate distributions when a specific portfolio is established. In this paper, the multivariate quantile vector is proposed to estimate VaR for multivariate distribution, and the Vector at Risk for multivariate space is defined based on the quantile vector. When a weight vector for a specific portfolio is given, one point among Vector at Risk could be found as the best VaR which is called as an alternative VaR. The alternative VaR proposed in this work is compared with the VaR of Morgan with bivariate and trivariate examples; in addition, some properties of the alternative VaR are also explored.
A sequential outlier detecting method using a clustering algorithm
Seo, Han Son ; Yoon, Min ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 699~706
DOI : 10.5351/KJAS.2016.29.4.699
Outlier detection methods without performing a test often do not succeed in detecting multiple outliers because they are structurally vulnerable to a masking effect or a swamping effect. This paper considers testing procedures supplemented to a clustering-based method of identifying the group with a minority of the observations as outliers. One of general steps is performing a variety of t-test on individual outlier-candidates. This paper proposes a sequential procedure for searching for outliers by changing cutoff values on a cluster tree and performing a test on a set of outlier-candidates. The proposed method is illustrated and compared to existing methods by an example and Monte Carlo studies.
Nonparametric procedures based on aligned method and placement for ordered alternatives in randomized block design
Kim, Hyosook ; Kim, Dongjae ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 707~717
DOI : 10.5351/KJAS.2016.29.4.707
Nonparametric procedures in a randomized block design was proposed by Friedman (1937) as a general alternative as well as suggested as a test for ordered alternatives by Page (1963). These methods are used for the rank of treatments in each block. In this paper, we proposed nonparametric procedures using aligned method proposed by Hodges and Lehmann (1962) to reduce among block information and based on placement suggested by Kim (1999) in a randomized block design. We also perform a Monte Carlo study to compare the empirical powers of the proposed procedures and established method.
Social network analysis of keyword community network in IoT patent data
Kim, Do Hyun ; Kim, Hyon Hee ; Kim, Donggeon ; Jo, Jinnam ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 719~728
DOI : 10.5351/KJAS.2016.29.4.719
In this paper, we analyzed IoT patent data using the social network analysis of keyword community network in patents related to Internet of Things technology. To identify the difference of IoT patent trends between Korea and USA, 100 Korea patents and 100 USA patents were collected, respectively. First, we first extracted important keywords from IoT patent abstracts using the TF-IDF weight and their correlation and then constructed the keyword network based on the selected keywords. Second, we constructed a keyword community network based on the keyword community and performed social network analysis. Our experimental results showed while Korea patents focus on the core technologies of IoT (such as security, semiconductors and image process areas), USA patents focus on the applications of IoT (such as the smart home, interactive media and telecommunications).
Nonparametric method in one-way layout based on joint placement
Jeon, Kyoung-Ah ; Kim, Dongjae ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 729~739
DOI : 10.5351/KJAS.2016.29.4.729
Kruskal and Wallis (1952) proposed a nonparametric method to test the differences between more than three independent treatments. This procedure uses rank in mixed sample combined with more than three unlike populations. This paper proposes a the new procedure based on joint placements for a one-way layout as extension of the joint placements described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed method with previous methods.
A numerical study of adjusted parameter estimation in normal inverse Gaussian distribution
Yoon, Jeongyoen ; Song, Seongjoo ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 741~752
DOI : 10.5351/KJAS.2016.29.4.741
Numerous studies have shown that normal inverse Gaussian (NIG) distribution adequately fits the empirical return distribution of financial securities. The estimation of parameters can also be done relatively easily, which makes the NIG distribution more useful in financial markets. The maximum likelihood estimation and the method of moments estimation are easy to implement; however, we may encounter a problem in practice when a relationship among the moments is violated. In this paper, we investigate this problem in the parameter estimation and try to find a simple solution through simulations. We examine the effect of our adjusted estimation method with real data: daily log returns of KOSPI, S&P500, FTSE and HANG SENG. We also checked the performance of our method by computing the value at risk of daily log return data. The results show that our method improves the stability of parameter estimation, while it retains a comparable performance in goodness-of-fit.
Performance analysis of EVT-GARCH-Copula models for estimating portfolio Value at Risk
Lee, Sang Hun ; Yeo, Sung Chil ;
Korean Journal of Applied Statistics, volume 29, issue 4, 2016, Pages 753~771
DOI : 10.5351/KJAS.2016.29.4.753
Value at Risk (VaR) is widely used as an important tool for risk management of financial institutions. In this paper we discuss estimation and back testing for VaR of the portfolio composed of KOSPI, Dow Jones, Shanghai, Nikkei indexes. The copula functions are adopted to construct the multivariate distributions of portfolio components from marginal distributions that combine extreme value theory and GARCH models. Volatility models with t distribution of the error terms using Gaussian, t, Clayton and Frank copula functions are shown to be more appropriate than the other models, in particular the model using the Frank copula is shown to be the best.