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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 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
Parrondo Paradox and Stock Investment
Cho, Dong-Seob ; Lee, Ji-Yeon ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 543~552
DOI : 10.5351/KJAS.2012.25.4.543
Parrondo paradox is a counter-intuitive phenomenon where two losing games can be combined to win or two winning games can be combined to lose. When we trade stocks with a history-dependent Parrondo game rule (where we buy and sell stocks based on recent investment outcomes) we found Parrondo paradox in stock trading. Using stock data of the KRX from 2008 to 2010, we analyzed the Parrondo paradoxical cases in the Korean stock market.
A Study of a Combining Model to Estimate Quarterly GDP
Kang, Chang-Ku ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 553~561
DOI : 10.5351/KJAS.2012.25.4.553
Various statistical models to Estimate GDP (measured as a nation's economic situation) have been developed. In this paper an autoregressive distributed lag model, factor model, and a Bayesian VAR model estimate quarterly GDP as a single model; the combined estimates were evaluated to compare a single model. Subsequently, we suggest that some combined models are better than a single model to estimate quarterly GDP.
Detecting Genetic Association and Gene-Gene Interaction using Network Analysis in Case-Control Study
Jin, Seo-Hoon ; Lee, Min-Hee ; Lee, Hyo-Jung ; Park, Mi-Ra ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 563~573
DOI : 10.5351/KJAS.2012.25.4.563
Various methods of analysis have been proposed to understand the gene-disease relation and gene-gene interaction effect for a disease through comparison of genotype in case-control study. In this study, we proposed the method to detect a genetic association and gene-gene interaction through the use of a network graph and centrality measures that are used in social network analysis. The applicability of the proposed method was studied through an analysis of real genetic data.
Statistical Methods in Non-Inferiority Trials - A Focus on US FDA Guidelines -
Kang, Seung-Ho ; Wang, So-Young ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 575~587
DOI : 10.5351/KJAS.2012.25.4.575
The effect of a new treatment is proven through the comparison of a new treatment with placebo; however, the number of parent non-inferiority trials tends to grow proportionally to the number of active controls. In a non-inferiority trial a new treatment is approved by proof that the new treatment is not inferior to an active control; however, both additional assumptions and historical trials are needed to show (through the comparison of the new treatment with the active control in a non-inferiority trial) that the new treatment is more efficacious than a putative placebo. The two different methods of using the historical data: frequentist principle method and meta-analytic method. This paper discusses the statistical methods and different Type I error rates obtained through the different methods employed.
A Resetting Scheme for Process Parameters using the Mahalanobis-Taguchi System
Park, Chang-Soon ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 589~603
DOI : 10.5351/KJAS.2012.25.4.589
Mahalanobis-Taguchi system(MTS) is a statistical tool for classifying the normal group and abnormal group in multivariate data structures. In addition to the classification itself, the MTS uses a method for selecting variables useful for the classification. This method can be used efficiently especially when the abnormal group data are scattered without a specific directionality. When the feedback adjustment procedure through the measurements of the process output for controlling process input variables is not practically possible, the reset procedure can be an alternative one. This article proposes a reset procedure using the MTS. Moreover, a method for identifying input variables to reset is also proposed by the use of the contribution. The identification of the root-cause parameters using the existing dimension-reduced contribution tends to be difficult due to the variety of correlation relationships of multivariate data structures. However, it became possible to provide an improved decision when used together with the location-centered contribution and the individual-parameter contribution.
A Comparison Study of Multivariate Binary and Continuous Outcomes
Pak, Dae-Woo ; Cho, Hyung-Jun ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 605~612
DOI : 10.5351/KJAS.2012.25.4.605
Multivariate data are often generated with multiple outcomes in various fields. Multiple outcomes could be mixed as continuous and discrete. Because of their complexity, the data are often dealt with by separately applying regression analysis to each outcome even though they are associated the each other. This univariate approach results in the low efficiency of estimates for parameters. We study the efficiency gains of the multivariate approaches relative to the univariate approach with the mixed data that include continuous and binary outcomes. All approaches yield consistent estimates for parameters with complete data. By jointly estimating parameters using multivariate methods, it is generally possible to obtain more accurate estimates for parameters than by a univariate approach. The association between continuous and binary outcomes creates a gap in efficiency between multivariate and univariate approaches. We provide a guidance to analyze the mixed data.
Multidimensional Scaling of Asymmetric Distance Matrices
Huh, Myung-Hoe ; Lee, Yong-Goo ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 613~620
DOI : 10.5351/KJAS.2012.25.4.613
In most cases of multidimensional scaling(MDS), the distances or dissimilarities among units are assumed to be symmetric. Thus, it is not an easy task to deal with asymmetric distances. Asymmetric MDS developed so far face difficulties in the interpretation of results. This study proposes a much simpler asymmetric MDS, that utilizes the notion of "altitude". The analogy arises in mountaineering: It is easier (more difficult) to move from the higher (lower) point to the lower (higher). The idea is formulated as a quantification problem, in which the disparity of distances is maximally related to the altitude difference. The proposed method is demonstrated in three examples, in which the altitudes are visualized by rainbow colors to ease the interpretability of users.
Nonparametric Inference for the Recurrent Event Data with Incomplete Observation Gaps
Kim, Jin-Heum ; Nam, Chung-Mo ; Kim, Yang-Jin ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 621~632
DOI : 10.5351/KJAS.2012.25.4.621
Recurrent event data can be easily found in longitudinal studies such as clinical trials, reliability fields, and the social sciences; however, there are a few observations that disappear temporarily in sight during the follow-up and then suddenly reappear without notice like the Young Traffic Offenders Program(YTOP) data collected by Farmer et al. (2000). In this article we focused on inference for a cumulative mean function of the recurrent event data with these incomplete observation gaps. Defining a corresponding risk set would be easily accomplished if we know the exact intervals where the observation gaps occur. However, when they are incomplete (if their starting times are known but their terminating times are unknown) we need to estimate a distribution function for the terminating times of the observation gaps. To accomplish this, we treated them as interval-censored and then estimated their distribution using the EM algorithm proposed by Turnbull (1976). We proposed a nonparametric estimator for the cumulative mean function and also a nonparametric test to compare the cumulative mean functions of two groups. Through simulation we investigated the finite-sample performance of the proposed estimator and proposed test. Finally, we applied the proposed methods to YTOP data.
Estimation of Relative Potency with the Parallel-Line Model
Lee, Tae-Won ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 633~640
DOI : 10.5351/KJAS.2012.25.4.633
Biological methods are described for the assay of certain substances and preparations whose potency cannot be adequately assured by chemical or physical analysis. The principle applied through these assays is of a comparison with a standard preparation to determine how much of the examined substance produces the same biological effects as a given quantity (the Unit) of the standard preparation. In these dilution assays, to estimate the relative potencies of the unknown preparations to the standard preparations, it is necessary to compare dose-response relationships of standard and unknown preparations. The dose-response relationship in the dilution assay is non-linear and sigmoid when a wide range of doses is applied. The parallel line model (applied to the dose region with the steepest slope) is used to estimate the relative potency. In this paper, the statistical theory in the parallel line model is explained with an application to a dilution assay data. The parallel line method is implemented in a SAS program and is available at the author's homepage(http://cafe.daum.net/go.analysis).
Bayes Inference for the Spatial Bilinear Time Series Model with Application to Epidemic Data
Lee, Sung-Duck ; Kim, Duk-Ki ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 641~650
DOI : 10.5351/KJAS.2012.25.4.641
Spatial time series data can be viewed as a set of time series simultaneously collected at a number of spatial locations. This paper studies Bayesian inferences in a spatial time bilinear model with a Gibbs sampling algorithm to overcome problems in the numerical analysis techniques of a spatial time series model. For illustration, the data set of mumps cases reported from the Korea Center for Disease Control and Prevention monthly over the years 2001~2009 are selected for analysis.
Modeling Circular Data with Uniformly Dispersed Noise
Yu, Hye-Kyung ; Jun, Kyoung-Ho ; Na, Jong-Hwa ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 651~659
DOI : 10.5351/KJAS.2012.25.4.651
In this paper we developed a statistical model for circular data with noises. In this case, model fitting by single circular model has a lack-of-fit problem. To overcome this problem, we consider some mixture models that include circular uniform distribution and apply an EM algorithm to estimate the parameters. Both von Mises and Wrapped skew normal distributions are considered in this paper. Simulation studies are executed to assess the suggested EM algorithms. Finally, we applied the suggested method to fit 2008 EHFRS(Epidemic Hemorrhagic Fever with Renal Syndrome) data provided by the KCDC(Korea Centers for Disease Control and Prevention).
Local Projective Display of Multivariate Numerical Data
Huh, Myung-Hoe ; Lee, Yong-Goo ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 661~668
DOI : 10.5351/KJAS.2012.25.4.661
For displaying multivariate numerical data on a 2D plane by the projection, principal components biplot and the GGobi are two main tools of data visualization. The biplot is very useful for capturing the global shape of the dataset, by representing
variables simultaneously on a single graph. The GGobi shows a dynamic movie of the images of
observations projected onto a sequence of unit vectors floating on the
-dimensional sphere. Even though these two methods are certainly very valuable, there are drawbacks. The biplot is too condensed to describe the detailed parts of the data, and the GGobi is too burdensome for ordinary data analyses. In this paper, "the local projective display(LPD)" is proposed for visualizing multivariate numerical data. Main steps of the LDP are 1)
-means clustering of the data into
subsets, 2) drawing
principal components biplots of individual subsets, and 3) sequencing
plots by Hurley's (2004) endlink algorithm for cognitive continuity.
Noise Removal using Support Vector Regression in Noisy Document Images
Kim, Hee-Hoon ; Kang, Seung-Hyo ; Park, Jai-Hyun ; Ha, Hyun-Ho ; Lim, Dong-Hoon ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 669~680
DOI : 10.5351/KJAS.2012.25.4.669
Noise removal of document images is a necessary step during preprocessing to recognize characters effectively because it has influences greatly on processing speed and performance for character recognition. We have considered using the spatial filters such as traditional mean filters and Gaussian filters, and wavelet transformed based methods for noise deduction in natural images. However, these methods are not effective for the noise removal of document images. In this paper, we present noise removal of document images using support vector regression. The proposed approach consists of two steps which are SVR training step and SVR test step. We construct an optimal prediction model using grid search with cross-validation in SVR training step, and then apply it to noisy images to remove noises in test step. We evaluate our SVR based method both quantitatively and qualitatively for noise removal in Korean, English and Chinese character documents, and compare it to some existing methods. Experimental results indicate that the proposed method is more effective and can get satisfactory removal results.
Families of Estimators of Finite Population Variance using a Random Non-Response in Survey Sampling
Singh, Housila P. ; Tailor, Rajesh ; Kim, Jong-Min ; Singh, Sarjinder ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 681~695
DOI : 10.5351/KJAS.2012.25.4.681
In this paper, a family of estimators for the finite population variance investigated by Srivastava and Jhajj (1980) is studied under two different situations of random non-response considered by Tracy and Osahan (1994). Asymptotic expressions for the biases and mean squared errors of members of the proposed family are obtained; in addition, an asymptotic optimum estimator(AOE) is also identified. Estimators suggested by Singh and Joarder (1998) are shown to be members of the proposed family. A correction to the Singh and Joarder (1998) results is also presented.
The Role of Artificial Observations in Misclassified Binary Data with Common False-Positive Error
Lee, Seung-Chun ;
Korean Journal of Applied Statistics, volume 25, issue 4, 2012, Pages 697~706
DOI : 10.5351/KJAS.2012.25.4.697
An Agresti-Coull type test is considered for the difference of binomial proportions in two doubly sampled data subject to common false-positive error. The performance of the test is compared with likelihood-based tests. The Agresti-Coull test has many desirable properties in that it can approximate the nominal significance level well, and has comparable power performance with a computational advantage.