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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 25, Issue 6 - Dec 2012
Volume 25, Issue 5 - Oct 2012
Volume 25, Issue 4 - Aug 2012
Volume 25, Issue 3 - Jun 2012
Volume 25, Issue 2 - Apr 2012
Volume 25, Issue 1 - Feb 2012
Selecting the target year
Estimation of Nonlinear Impulse Responses of Stock Indices by Asset Class
Chang, Young-Jae ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 239~249
DOI : 10.5351/KJAS.2012.25.2.239
We estimate nonlinear impulse responses of stock indices by asset class by the Local Projection method as suggested by Jorda (2005) to compute impulse responses. The method estimates impulse responses without the specification and estimation of the underlying multivariate dynamic system unlike the usual way of vector autoregression(VAR). It estimates Local Projections at each period of interest rather than extrapolating into increasingly distant horizons with the advantages of easy estimation and non-linear flexible specification. The Local Projection method adequately captures the nonlinearity and asymmetry of the impulse responses of the stock indices compared to those from VARs.
A Study on the Tourism Combining Demand Forecasting Models for the Tourism in Korea
Son, H.G. ; Ha, M.H. ; Kim, S. ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 251~259
DOI : 10.5351/KJAS.2012.25.2.251
This paper applies forecasting models such as ARIMA, Holt-Winters and AR-GARCH models to analyze daily tourism data in Korea. To evaluate the performance of the models, we need single and double seasonal models that compare the RMSE and SE for a better accuracy of the forecasting models based on Armstrong (2001).
Cutpoint Selection via Penalization in Credit Scoring
Jin, Seul-Ki ; Kim, Kwang-Rae ; Park, Chang-Yi ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 261~267
DOI : 10.5351/KJAS.2012.25.2.261
In constructing a credit scorecard, each characteristic variable is divided into a few attributes; subsequently, weights are assigned to those attributes in a process called coarse classification. While partitioning a characteristic variable into attributes, one should determine appropriate cutpoints for the partition. In this paper, we propose a cutpoint selection method via penalization. In addition, we compare the performances of the proposed method with classification spline machine (Koo et al., 2009) on both simulated and real credit data.
A Method for Gene Group Analysis and Its Application
Lee, Tae-Won ; Delongchamp, Robert R. ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 269~277
DOI : 10.5351/KJAS.2012.25.2.269
In microarray data analysis, recent efforts have focused on the discovery of gene sets from a pathway or functional categories such as Gene Ontology terms(GO terms) rather than on individual gene function for its direct interpretation of genome-wide expression data. We introduce a meta-analysis method that combines
-values for changes of each gene in the group. The method measures the significance of overall treatment-induced change in a gene group. An application of the method to a real data demonstrates that it has benefits over other statistical methods such as Fisher's exact test and permutation methods. The method is implemented in a SAS program and it is available on the author's homepage(http://cafe.daum.net/go.analysis).
Pattern-Mixture Model of the Cox Proportional Hazards Model with Missing Binary Covariates
Youk, Tae-Mi ; Song, Ju-Won ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 279~291
DOI : 10.5351/KJAS.2012.25.2.279
When fitting a Cox proportional hazards model with missing covariates, it is inefficient to exclude observations with missing values in the analysis. Furthermore, if the missing-data mechanism is not Missing Completely At Random(MCAR), it may lead to biased parameter estimation. Many approaches have been suggested to handle the Cox proportional hazards model when covariates are sometimes missing, but they are based on the selection model. This paper suggest an approach to handle Cox proportional hazards model with missing covariates by using the pattern-mixture model (Little, 1993). The pattern-mixture model is expressed by the joint distribution of survival time and the missing-data mechanism. In the pattern-mixture model, many models can be considered by setting up various restrictions, and different results under various restrictions indicate the sensitivity of the model due to missing covariates. A simulation study was conducted to show the sensitivity of parameter estimation under different restrictions in a pattern-mixture model. The proposed approach was also applied to mouse leukemia data.
Two Stage Small Area Estimation
Lee, Sang-Eun ; Shin, Key-Il ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 293~300
DOI : 10.5351/KJAS.2012.25.2.293
When Binomial data are obtained, logit and logit mixed models are commonly used for small area estimation. Those models are known to have good statistical properties through the use of unit level information; however, data should be obtained as area level in order to use area level information such as spatial correlation or auto-correlation. In this research, we suggested a new small area estimator obtained through the combination of unit level information with area level information.
Some Alternative Classes of Shrinkage Estimators for a Scale Parameter of the Exponential Distribution
Singh, Housila P. ; Singh, Sarjinder ; Kim, Jong-Min ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 301~309
DOI : 10.5351/KJAS.2012.25.2.301
This paper proposes some alternative classes of shrinkage estimators and analyzes their properties. In particular, some new shrinkage estimators are identified and compared with Pandey (1983), Pandey and Srivastav (1985) and Jani (1991) estimators. Numerical illustrations are also provided.
Testing Exponentiality Based on EDF Statistics for Randomly Censored Data when the Scale Parameter is Unknown
Kim, Nam-Hyun ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 311~319
DOI : 10.5351/KJAS.2012.25.2.311
The simplest and the most important distribution in survival analysis is exponential distribution. Koziol and Green (1976) derived Cram
r-von Mises statistic's randomly censored version based on the Kaplan-Meier product limit estimate of the distribution function; however, it could not be practical for a real data set since the statistic is for testing a simple goodness of fit hypothesis. We generalized it to the composite hypothesis for exponentiality with an unknown scale parameter. We also considered the classical Kolmogorov-Smirnov statistic and generalized it by the exact same way. The two statistics are compared through a simulation study. As a result, we can see that the generalized Koziol-Green statistic has better power in most of the alternative distributions considered.
Basic Statistics in Quantile Regression
Kim, Jae-Wan ; Kim, Choong-Rak ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 321~330
DOI : 10.5351/KJAS.2012.25.2.321
In this paper we study some basic statistics in quantile regression. In particular, we investigate the residual, goodness-of-fit statistic and the effect of one or few observations on estimates of regression coefficients. In addition, we compare the proposed goodness-of-fit statistic with the statistic considered by Koenker and Machado (1999). An illustrative example based on real data sets is given to see the numerical performance of the proposed basic statistics.
A Robust Approach of Regression-Based Statistical Matching for Continuous Data
Sohn, Soon-Cheol ; Jhun, Myoung-Shic ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 331~339
DOI : 10.5351/KJAS.2012.25.2.331
Statistical matching is a methodology used to merge microdata from two (or more) files into a single matched file, the variants of which have been extensively studied. Among existing studies, we focused on Moriarity and Scheuren's (2001) method, which is a representative method of statistical matching for continuous data. We examined this method and proposed a revision to it by using a robust approach in the regression step of the procedure. We evaluated the efficiency of our revised method through simulation studies using both simulated and real data, which showed that the proposed method has distinct advantages over existing alternatives.
Bootstrapping Composite Quantile Regression
Seo, Kang-Min ; Bang, Sung-Wan ; Jhun, Myoung-Shic ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 341~350
DOI : 10.5351/KJAS.2012.25.2.341
Composite quantile regression model is considered for iid error case. Since the regression coefficients are the same across different quantiles, composite quantile regression can be used to combine the strength across multiple quantile regression models. For the composite quantile regression, bootstrap method is examined for statistical inference including the selection of the number of quantiles and confidence intervals for the regression coefficients. Feasibility of the bootstrap method is demonstrated through a simulation study.
Future Weather Generation with Spatio-Temporal Correlation for the Four Major River Basins in South Korea
Lee, Dong-Hwan ; Lee, Jae-Yong ; Oh, Hee-Seok ; Lee, Young-Jo ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 351~362
DOI : 10.5351/KJAS.2012.25.2.351
Weather generators are statistical tools to produce synthetic sequences of daily weather variables. We propose the multisite weather generators with a spatio-temporal correlation based on hierarchical generalized linear models. We develop a computational algorithm to produce future weather variables that use three different types of green-house gases scenarios. We apply the proposed method to a daily time series of precipitation and average temperature for South Korea.
Bayesian Analysis of Korean Alcohol Consumption Data Using a Zero-Inflated Ordered Probit Model
Oh, Man-Suk ; Oh, Hyun-Tak ; Park, Se-Mi ;
Korean Journal of Applied Statistics, volume 25, issue 2, 2012, Pages 363~376
DOI : 10.5351/KJAS.2012.25.2.363
Excessive zeroes are often observed in ordinal categorical response variables. An ordinary ordered Probit model is not appropriate for zero-inflated data especially when there are many different sources of generating 0 observations. In this paper, we apply a two-stage zero-inflated ordered Probit (ZIOP) model which incorporate the zero-flated nature of data, propose a Bayesian analysis of a ZIOP model, and apply the method to alcohol consumption data collected by the National Bureau of Statistics, Korea. In the first stage of a ZIOP model, a Probit model is introduced to divide the non-drinkers into genuine non-drinkers who do not participate in drinking due to personal beliefs or permanent health problems and potential drinkers who did not drink at the time of the survey but have the potential to become drinkers. In the second stage, an ordered probit model is applied to drinkers that consists of zero-consumption potential drinkers and positive consumption drinkers. The analysis results show that about 30% of non-drinkers are genuine non-drinkers and hence the Korean alcohol consumption data has the feature of zero-inflated data. A study on the marginal effect of each explanatory variable shows that certain explanatory variables have effects on the genuine non-drinkers and potential drinkers in opposite directions, which may not be detected by an ordered Probit model.