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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 26, Issue 6 - Dec 2013
Volume 26, Issue 5 - Oct 2013
Volume 26, Issue 4 - Aug 2013
Volume 26, Issue 3 - Jun 2013
Volume 26, Issue 2 - Apr 2013
Volume 26, Issue 1 - Feb 2013
Selecting the target year
Analysis on the Effect of Unit Non-Response Adjustment using the Survey of Household Finances
Baek, Jeeseon ; Shim, Kyuho ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 375~387
DOI : 10.5351/KJAS.2013.26.3.375
Unit non-response of surveys reduces the efficiency of the estimates and also causes non-response bias especially when there is large difference between respondents and non-respondents. Non-response weighting adjustments have usually been used to compensate for non-response. It is not easy to examine the non-response bias as well as to obtain information on the non-respondents in sample surveys. A household panel survey, called The Survey of Household Finances, was conducted in both 2010 and 2011. In this paper, we assume that non-response households in Wave 2 have strong non-response (non-cooperative) tendency. We classify those households into non-response households in Wave 1. Under this assumption, the characteristics of non-response households, the non-response bias and the effect of non-response adjustments are investigated.
Screening and Clustering for Time-course Yeast Microarray Gene Expression Data using Gaussian Process Regression
Kim, Jaehee ; Kim, Taehoun ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 389~399
DOI : 10.5351/KJAS.2013.26.3.389
This article introduces Gaussian process regression and shows its application with time-course microarray gene expression data. Gene screening for yeast cell cycle microarray expression data is accomplished with a ratio of log marginal likelihood that uses Gaussian process regression with a squared exponential covariance kernel function. Gaussian process regression fitting with each gene is done and shown with the nine top ranking genes. With the screened data the Gaussian model-based clustering is done and its silhouette values are calculated for cluster validity.
Identifying the Chickens-Eggs Statistical Lead-Lag Dilemma
Kim, Tae Ho ; Kim, Min Jeong ; Lee, Jeen Woan ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 401~411
DOI : 10.5351/KJAS.2013.26.3.401
This study investigates the controversial chickens-eggs dilemma and empirically performs statistical tests to examine if there exists a causality between them. Granger and Hsiao tests are applied to both level and stationary variables to identify the lead-lag relationships. Each of these test is found to have the robust result where the causality runs from eggs to chickens; in addition, the explanatory power of one variable in variations of the other appears to remain time invariant. The outcome is proved to be valid as the hypothesis test for no structural change in their relationship fails to be rejected.
A Modeling of Daily Temperature in Seoul using GLM Weather Generator
Kim, Hyeonjeong ; Do, Hae Young ; Kim, Yongku ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 413~420
DOI : 10.5351/KJAS.2013.26.3.413
Stochastic weather generator is a commonly used tool to simulate daily weather time series. Recently, a generalized linear model(GLM) has been proposed as a convenient approach to tting these weather generators. In the present paper, a stochastic weather generator is considered to model the time series of daily temperatures for Seoul South Korea. As a covariate, precipitation occurrence is introduced to a relate short-term predictor to short-term predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate a time series of seasonal mean temperatures in the GLM weather generator as a covariate.
A Development of Object-Oriented, Dynamically Linked Statistical Package for 5-8 Graders
Lee, Jung Jin ; Lee, Tae Rim ; Kang, Gunseog ; Kim, Sungsoo ; Park, Heon Jin ; Lee, Yoon-Dong ; Sim, Songyong ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 421~429
DOI : 10.5351/KJAS.2013.26.3.421
Modern statistics is used in many fields; however many users face difficulties in understanding statistical concepts. On the other hand, elementary school curriculum covers stem and leaf plot, pie chart, charts for proportional data as well as descriptive statistics including the mean. We find that an "intuitive" statistical package focused on 5-8 graders for statistical education will help future statistics users understand statistical concepts at earlier stages of their lives.
A Parameter Estimation Method using Nonlinear Least Squares
Oh, Suna ; Song, Jongwoo ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 431~440
DOI : 10.5351/KJAS.2013.26.3.431
We consider the problem of estimating the parameters of heavy tailed distributions. In general, maximum likelihood estimation(MLE) is the most preferred method of parameter estimation because it has good properties such as asymptotic consistency, normality and efficiency. However, MLE is not always the best solution because MLE is unstable or does not exist in some cases. This paper proposes another parameter estimation method, non-linear least squares(NLS) and compares its performance to MLE. The NLS estimator is achieved by minimizing sum of squared difference between empirical cumulative distribution function(CDF) and a theoretical distribution function. In this article, we compare the NLS method to MLE using simulated data from heavy tailed distributions. The NLS method is shown to perform better than MLE in Burr distribution when the sample size is small; in addition, it performs well in a Frechet distribution.
A Study for the Drivers of Movie Box-office Performance
Kim, Yon Hyong ; Hong, Jeong Han ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 441~452
DOI : 10.5351/KJAS.2013.26.3.441
This study analyzed the relationship between key film and a box office record success factors based on movies released in the first quarter of 2013 in Korea. An over-fitting problem can happen if there are too many explanatory variables inserted to regression model; in addition, there is a risk that the estimator is instable when there is multi-collinearity among the explanatory variables. For this reason, optimal variable selection based on high explanatory variables in box-office performance is of importance. Among the numerous ways to select variables, LASSO estimation applied by a generalized linear model has the smallest prediction error that can efficiently and quickly find variables with the highest explanatory power to box-office performance in order.
Bayesian Analysis of Dose-Effect Relationship of Cadmium for Benchmark Dose Evaluation
Lee, Minjea ; Choi, Taeryon ; Kim, Jeongseon ; Woo, Hae Dong ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 453~470
DOI : 10.5351/KJAS.2013.26.3.453
In this paper, we consider a Bayesian analysis of the dose-effect relationship of cadmium to evaluate a benchmark dose(BMD). For this purpose, two dose-response curves commonly used in the toxicity study are fitted based on Bayesian methods to the data collected from the scientific literature on cadmium toxicity. Specifically, Bayesian meta-analysis and hierarchical modeling build an overall dose-effect relationship that use a piecewise linear model and Hill model, where the inter-study heterogeneity and inter-individual variability of dose and effect such as gender, age and ethnicity are accounted. Estimation of the unknown parameters is made by using a Markov chain Monte Carlo algorithm based user-friendly software WinBUGS. Benchmark dose estimates are evaluated for various cut-offs and compared with different tested subpopulations with with gender, age and ethnicity based on these two Bayesian hierarchical models.
An One-factor VaR Model for Stock Portfolio
Park, Keunhui ; Ko, Kwangyee ; Beak, Jangsun ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 471~481
DOI : 10.5351/KJAS.2013.26.3.471
The current VaR Model based on J. P. Morgan's RiskMetrics has problem that actual loss exceeds VaR under unstable economic conditions because the current VaR Model can't re ect future economic conditions. In general, any corporation's stock price is determined by the rm's idiosyncratic factor as well as the common systematic factor that in uences all stocks in the portfolio. In this study, we propose an One-factor VaR Model for stock portfolio which is decomposed into the common systematic factor and the rm's idiosyncratic factor. We expect that the actual loss will not exceed VaR when the One-factor Model is implemented because the common systematic factor considering the future economic conditions is estimated. Also, we can allocate the stock portfolio to minimize the loss.
Value at Risk with Peaks over Threshold: Comparison Study of Parameter Estimation
Kang, Minjung ; Kim, Jiyeon ; Song, Jongwoo ; Song, Seongjoo ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 483~494
DOI : 10.5351/KJAS.2013.26.3.483
The importance of financial risk management has been highlighted after several recent incidences of global financial crisis. One of the issues in financial risk management is how to measure the risk; currently, the most widely used risk measure is the Value at Risk(VaR). We can consider to estimate VaR using extreme value theory if the financial data have heavy tails as the recent market trend. In this paper, we study estimations of VaR using Peaks over Threshold(POT), which is a common method of modeling fat-tailed data using extreme value theory. To use POT, we first estimate parameters of the Generalized Pareto Distribution(GPD). Here, we compare three different methods of estimating parameters of GPD by comparing the performance of the estimated VaR based on KOSPI 5 minute-data. In addition, we simulate data from normal inverse Gaussian distributions and examine two parameter estimation methods of GPD. We find that the recent methods of parameter estimation of GPD work better than the maximum likelihood estimation when the kurtosis of the return distribution of KOSPI is very high and the simulation experiment shows similar results.
On the Application of Multivariate Kendall's Tau and Its Interpretation
Lee, Woojoo ; Ahn, Jae Youn ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 495~509
DOI : 10.5351/KJAS.2013.26.3.495
We study multivariate extension of Kendall's tau and its statistical interpretation. There exist various versions of multivariate Kendall's tau, for example Scarsini (1984), Joe (1990) and Genest et al. (2011); however, few of them mention its lower bounds. For the bivariate case, the Fr
chet-Hoeffding lower bound can achieve the lower bound of Kendall's tau. However in the multivariate case, the Fr
chet-Hoeffding lower bound itself does not exist as a distribution, which makes the interpretation of Kendall's tau unclear when it has negative value. In this paper, we explain sufficient conditions to achieve the lower bound of Kendall's tau and provide real data examples that provide further insights into the interpretation for the lower bounds of Kendall's tau.
On Rice Estimator in Simple Regression Models with Outliers
Park, Chun Gun ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 511~520
DOI : 10.5351/KJAS.2013.26.3.511
Detection outliers and robust estimators are crucial in regression models with outliers. In such studies the focus is on detecting outliers and estimating the coefficients using leave-one-out. Our study introduces Rice estimator which is an error variance estimator without estimating the coefficients. In particular, we study a comparison of the statistical properties for Rice estimator with and without outliers in simple regression models.
Test of Homogeneity for Panel Bilinear Time Series Model
Lee, ShinHyung ; Kim, SunWoo ; Lee, SungDuck ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 521~529
DOI : 10.5351/KJAS.2013.26.3.521
The acceptance of the test of the homogeneity for panel time series models allows for the pooling of the series to achieve parsimony. In this paper, we introduce a panel bilinear time series model as well as derive the stationary condition and the limiting distribution of the test statistic of the homogeneity test for the model. For the applications study, we use Korea Mumps data from January 2001 to December 2008. Finally, we perform test of homogeneity for the panel data with 8 independent bilinear time series.
An Analysis of a Reverse Mortgage using a Multiple Life Model
Baek, HyeYoun ; Lee, SeonJu ; Lee, Hangsuck ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 531~547
DOI : 10.5351/KJAS.2013.26.3.531
Multiple life models are useful in multiple life insurance and multiple life annuities when the payment times of benets in these insurance products are contingent on the future life times of at least two people. A reverse mortgage is an annuity whose monthly payments terminate at the death time of the last survivor; however, actuaries have used female life table to calculate monthly payments of a reverse mortgage. This approach may overestimate monthly payments. This paper suggests a last-survivor life table rather than a female life table to avoid the overestimation of monthly payments. Next, this paper derives the distribution of the future life time of last survivor, and calculates the expected life times of male, female and last survivor. This paper calculates principal limits and monthly payments in cases of male life table, female life table and last-survivor life table, respectively. Some numerical examples are discussed.
Conversion between Decrement Models using Cubic Spline
Kim, Ju Kyung ; Lee, Hangsuck ;
Korean Journal of Applied Statistics, volume 26, issue 3, 2013, Pages 549~568
DOI : 10.5351/KJAS.2013.26.3.549
This paper discusses conversion methods between multiple decrement models and associated single decrement models. One of most popular assumptions on fractional age is UDD(uniform distribution of decrement) or constant force of mortality in actuarial practice. Instead of these assumptions, this paper suggests cubic spline interpolation to approximate the distribution of fractional age with the continuous force of decrements. Conversion formulas are derived. The comparisons of these two methods based on the numerical data show that the cubic spline interpolation approach is more accurate.