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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Korean Journal of Applied Statistics
Journal Basic Information
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
Robust tests for heteroscedasticity using outlier detection methods
Seo, Han Son ; Yoon, Min ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 399~408
DOI : 10.5351/KJAS.2016.29.3.399
There is a need to detect heteroscedasticity in a regression analysis; however, it invalidates the standard inference procedure. The diagnostics on heteroscedasticity may be distorted when both outliers and heteroscedasticity exist. Available heteroscedasticity detection methods in the presence of outliers usually use robust estimators or separating outliers from the data. Several approaches have been suggested to identify outliers in the heteroscedasticity problem. In this article conventional tests on heteroscedasticity are modified by using a sequential outlier detection methods to separate outliers from contaminated data. The performance of the proposed method is compared with original tests by a Monte Carlo study and examples.
An analysis of determinants of purchase intension of individual pension using structural equation model
Lee, Chanhee ; Jung, Hongjoo ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 409~424
DOI : 10.5351/KJAS.2016.29.3.409
This study analyzed the causal relationship among factors that influence the purchase intensions for individual pensions which have a growing importance as a financial means after retirement. For this purpose, structural equation modeling based on the survey data (N
Analysis of the Korean peninsula precipitation using inverse statistics methodology
Min, Seungsik ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 425~435
DOI : 10.5351/KJAS.2016.29.3.425
In this paper, we analyze the inverse statistics of rainfall for 12 regions from 1973 to 2014. We obtain a probability density function f(x) of daily rainfall x, and
of the first passage time
for a given
. Lastly, we derive the relation between
, i.e., the averaged value of
. The analyses result in the x and
have stretched exponential distributions. Also,
has the form of a stretched exponential function. We derive the shape parameter
of the distribution, and analyze the characteristics of 12 regional rainfalls.
Identifying statistically significant gene sets based on differential expression and differential coexpression
Lee, Sunho ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 437~448
DOI : 10.5351/KJAS.2016.29.3.437
Gene set analysis utilizing biologic information is expected to produce more interpretable results because the occurrence of tumors (or diseases) is believed to be associated with the regulation of related genes. Many methods have been developed to identify statistically significant gene sets across different phenotypes; however, most focus exclusively on either the differential gene expression or the differential correlation structure in the gene set. This research provides a new method that simultaneously considers the differential expression of genes and differential coexpression with multiple genes in the gene set. Application of this NEW method is illustrated with real microarray data example, p53; subsequently, a simulation study compares its type I error rate and power with GSEA, SAMGS, GSCA and GSNCA.
Bootstrap estimation of long-run variance under strong dependence
Baek, Changryong ; Kwon, Yong ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 449~462
DOI : 10.5351/KJAS.2016.29.3.449
This paper considers a long-run variance estimation using a block bootstrap method under strong dependence also known as long range dependence. We extend currently available methods in two ways. First, it extends bootstrap methods under short range dependence to long range dependence. Second, to accommodate the observation that strong dependence may come from deterministic trend plus noise models, we propose to utilize residuals obtained from the nonparametric kernel estimation with the bimodal kernel. The simulation study shows that our method works well; in addition, a data illustration is presented for practitioners.
Robust spectral estimator from M-estimation point of view: application to the Korean housing price index
Pak, Ro Jin ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 463~470
DOI : 10.5351/KJAS.2016.29.3.463
In analysing a time series on the frequency domain, the spectral estimator (or periodogram) is a very useful statistic to identify the periods of a time series. However, the spectral estimator is very sensitive in nature to outliers, so that the spectral estimator in terms of M-estimation has been studied by some researchers. Pak (2001) proposed an empirical method to choose a tuning parameter for the Huber`s M-estimating function. In this article, we try to implement Pak`s estimation proposal in the spectral estimator. We use the Korean housing price index as an example data set for comparing various M-estimating results.
A comparison study of various robust regression estimators using simulation
Jang, Soohee ; Yoon, Jungyeon ; Chun, Heuiju ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 471~485
DOI : 10.5351/KJAS.2016.29.3.471
Least squares (LS) regression is a classic method for regression that is optimal under assumptions of regression and usual observations. However, the presence of unusual data in the LS method leads to seriously distorted estimates. Therefore, various robust estimation methods are proposed to circumvent the limitations of traditional LS regression. Among these, there are M-estimators based on maximum likelihood estimation (MLE), L-estimators based on linear combinations of order statistics and R-estimators based on a linear combinations of the ordered residuals. In this paper, robust regression estimators with high breakdown point and/or with high efficiency are compared under several simulated situations. The paper analyses and compares distributions of estimates as well as relative efficiencies calculated from mean squared errors (MSE) in the simulation study. We conclude that MM-estimators or GR-estimators are a good choice for the real data application.
Network analysis of urban-to-rural migration
Lee, Hyunsoo ; Roh, Jaesun ; Jung, Jin Hwa ; Jang, Woncheol ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 487~503
DOI : 10.5351/KJAS.2016.29.3.487
Urban-to-rural migration for farming has recently emerged as a new way to vitalize rural economies in a fast-aging rural Korea. In this paper, we analyze the 2013 data of returning farmers with statistical network methods. We identify urban to rural migration hubs with centrality measures and find migration trends based on regional clusters with similar features via statistical network models. We also fit a latent distance model to investigate the role of distance in migration.
Choice of weights in a hybrid volatility based on high-frequency realized volatility
Yoon, J.E. ; Hwang, S.Y. ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 505~512
DOI : 10.5351/KJAS.2016.29.3.505
The paper is concerned with high frequency financial time series. A weighted hybrid volatility is suggested to compute daily volatilities based on high frequency data. Various realized volatility (RV) computations are reviewed and the weights are chosen by minimizing the differences between the hybrid volatility and the realized volatility. A high frequency time series of KOSPI200 index is illustrated via QLIKE and Theil-U statistics.
An approximate fitting for mixture of multivariate skew normal distribution via EM algorithm
Kim, Seung-Gu ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 513~523
DOI : 10.5351/KJAS.2016.29.3.513
Fitting a mixture of multivariate skew normal distribution (MSNMix) with multiple skewness parameter vectors via EM algorithm often requires a highly expensive computational cost to calculate the moments and probabilities of multivariate truncated normal distribution in E-step. Subsequently, it is common to fit an asymmetric data set with MSNMix with a simple skewness parameter vector since it allows us to compute them in E-step in an univariate manner that guarantees a cheap computational cost. However, the adaptation of a simple skewness parameter is unrealistic in many situations. This paper proposes an approximate estimation for the MSNMix with multiple skewness parameter vectors that also allows us to treat them in an univariate manner. We additionally provide some experiments to show its effectiveness.
EMD based hybrid models to forecast the KOSPI
Kim, Hyowon ; Seong, Byeongchan ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 525~537
DOI : 10.5351/KJAS.2016.29.3.525
The paper considers a hybrid model to analyze and forecast time series data based on an empirical mode decomposition (EMD) that accommodates complex characteristics of time series such as nonstationarity and nonlinearity. We aggregate IMFs using the concept of cumulative energy to improve the interpretability of intrinsic mode functions (IMFs) from EMD. We forecast aggregated IMFs and residue with a hybrid model that combines the ARIMA model and an exponential smoothing method (ETS). The proposed method is applied to forecast KOSPI time series and is compared to traditional forecast models. Aggregated IMFs and residue provide a convenience to interpret the short, medium and long term dynamics of the KOSPI. It is also observed that the hybrid model with ARIMA and ETS is superior to traditional and other types of hybrid models.
Comparison of binary data imputation methods in clinical trials
An, Koosung ; Kim, Dongjae ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 539~547
DOI : 10.5351/KJAS.2016.29.3.539
We discussed how to handle missing binary data clinical trials. Patterns of occurring missing data are discussed and introduce missing binary data imputation methods that include the modified method. A simulation is performed by modifying actual data for each method. The condition of this simulation is controlled by a response rate and a missing value rate. We list the simulation results for each method and discussed them at the end of this paper.
A comparison of imputation methods for the consecutive missing temperature data
Kim, Hee-Kyung ; Kang, In-Kyeong ; Lee, Jae-Won ; Lee, Yung-Seop ;
Korean Journal of Applied Statistics, volume 29, issue 3, 2016, Pages 549~557
DOI : 10.5351/KJAS.2016.29.3.549
Consecutive missing values are likely to occur in long climate data due to system error or defective equipment. Furthermore, it is difficult to impute missing values. However, these complicated problems can be overcame by imputing missing values with reference time series. Reference time series must be composed of similar time series to time series that include missing values. We performed a simulation to compare three missing imputation methods (the adjusted normal ratio method, the regression method and the IDW method) to complete the missing values of time series. A comparison of the three missing imputation methods for the daily mean temperatures at 14 climatological stations indicated that the IDW method was better thanx others at south seaside stations. We also found the regression method was better than others at most stations (except south seaside stations).