• Bulletin of the Korean Mathematical Society
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• v.53 no.2
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• pp.351-363
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• 2016

In this paper, we consider the entropy-based goodness of fit test (Vasicek's test) for a composite hypothesis. The test measures the discrepancy between the nonparametric entropy estimate and the parametric entropy estimate obtained from an assumed parametric family of distributions. It is shown that the proposed test is asymptotically normal under regularity conditions, but is affected by parameter estimates. As a remedy, a bootstrap version of Vasicek's test is proposed. Simulation results are provided for illustration.

• International Commerce and Information Review
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• v.12 no.1
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• pp.99-121
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• 2010

This paper reviews the categories and properties of risk measures, analyzes the classes and structural equations of volatility forecasting models, and presents the practical methodologies and their expansion methods of estimating and forecasting the volatilities of exchange rates using Excel spreadsheet modeling. We apply the GARCH(1,1) model to the Korean won(KRW) denominated daily and monthly exchange rates of USD, JPY, EUR, GBP, CAD and CNY during the periods from January 4, 1998 to December 31, 2009, make the estimates of long-run variances in the returns of exchange rate calculated as the step-by-step change rate, and test the adequacy of estimated GARCH(1,1) model using the Box-Pierce-Ljung statistics Q and chi-square test-statistics. We demonstrate the adequacy of GARCH(1,1) model in estimating and forecasting the volatility of exchange rates in the monthly series except the semi-variance GARCH(1,1) applied to KRW/JPY100 rate. But we reject the adequacy of GARCH(1,1) model in estimating and forecasting the volatility of exchange rates in the daily series because of the very high Box-Pierce-Ljung statistics in the respective time lags resulting to the self-autocorrelation. In conclusion, the GARCH(1,1) model provides for the easy and helpful tools to forecast the exchange rate volatilities and may become the powerful methodology to overcome the application difficulties with the spreadsheet modeling.

• Journal of Oriental Neuropsychiatry
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• v.18 no.3
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• pp.15-21
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• 2007

This study was done to review problems in terms of statistical description and statistical application and analysis. Methods; The authors reviewed 42 statistical clinical study papers excluding 12 Overview papers, 75 Descriptive papers, 48 Animal studies out of 177 papers in the Journal of Oriental Neuropsychiatry in the 5 years from 2002 to 2006. Results : 1) 3 papers(7.1%) had no description of statistical method, only P-values, 25 papaers(59.5%) had tables without description of statistical method, 1 paper (2.3%) had no description of statistical method in study method. 2) 10 papers(23.8%) contained problems in terms of statistical application and analysis. 6papers (6/23, 26.0%) for Student t-test, 2 papers(2/7 28.6%)for $X^2$- test, 1 paper(1/15 6.7%) for the analysis of variance, 1 paper(1/6 16.7%) for Pearson correlation contained statistical problems. Conclusion : It was suggested that consultation of investigators with statisticians and more extensive statistical refereeing, the form of the guidelines for description and application of statistics are needed.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1065-1075
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• 2008

For the model validation of credit rating models, Kolmogorov-Smirnov(K-S) statistic has been widely used as a testing method of discriminatory power from the probabilities of default for default and non-default. For the credit rating works, K-S statistics are to test two identical distribution functions which are partitioned from a distribution. In this paper under the assumption that the distribution is known, modified K-S statistic which is formulated by using known distributions is proposed and compared K-S statistic.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.541-550
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• 2005

In this paper, we address the derivation of joint distributions and correlation coefficients for four pairs of statistics used commonly in a number of signal detection schemes. The upper and lower bounds of the correlation coefficients are obtained, and interesting relationships between the correlation coefficients are derived. Explicit values of the correlation coefficients are given in the form of tables and figures for easy reference. The results in this paper should be useful in comparing various detection statistics.

• International Journal of Reliability and Applications
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• v.8 no.2
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• pp.153-173
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• 2007

Wood-plastic composites (WPC) are gaining market share in the building industry because of durability/maintenance advantages of WPC over traditional wood products and because of the removal of chromated copper arsenate (CCA) pressure-treated wood from the market. In order to ensure continued market share growth, WPC manufacturers need greater focus on reliability, quality, and cost. The reliability methods outlined in this paper can be used to improve the quality of WPC and lower manufacturing costs by reducing raw material inputs and minimizing WPC waste. Statistical methods are described for analyzing stiffness (tangent modulus of elasticity: MOE) and flexural strength (modulus of rupture: MOR) test results on sampled WPC panels. Descriptive statistics, graphs, and reliability plots from these test data are presented and interpreted. Sources of variability in the MOE and MOR of WPC are suggested. The methods outlined may directly benefit WPC manufacturers through a better understanding of strength and stiffness measures, which can lead to process improvements and, ultimately, a superior WPC product with improved reliability, thereby creating greater customer satisfaction.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.13-19
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• 2002

In nonparametric entropy estimation, both mass and mean-preserving maximum entropy distribution (Theil, 1980) and the underlying distribution of the sample entropy (Vasicek, 1976), the most widely used entropy estimator, consist of nb mass-preserving densities based on disjoint Intervals of the simple averages of two adjacent order statistics. In this paper, we notice that those nonparametric density functions do not actually keep the mass-preserving constraint, and propose a modified sample entropy by considering the generalized 0-statistics (Kaigh and Driscoll, 1987) in averaging two adjacent order statistics. We consider the proposed estimator in a goodness of fit test for normality and compare its performance with that of the sample entropy.

• Genomics & Informatics
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• v.14 no.4
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• pp.187-195
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• 2016

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's $T^2$ test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.361-368
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• 2006

There are lots of two and three dimensional graph representing two dimensional categorical data. Among them, Li, et al. (2003) proposed Diamond Graph that projects three dimensional graph into two dimension whereby the third dimension is replaced with a diamond shape whose area and middle and vertical and horizontal lengths represent the outcome. In this paper, we use the Diamond graph to test the independence of two predictor variables for two dimensional data. And this graph could be applied for finding the best fitted log-linear model to three dimensional data.

• The Korean Journal of Applied Statistics
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• v.8 no.2
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• pp.147-161
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• 1995

The primary objective of this paper is to review parametric models and test statistics related to overdspersion of count data. Poisson or binomial assumption often fails to explain overdispersion. We reviewed real examples of overdispersion in count data that occurred in toxicological or teratological experiments. We also reviewed several models that were suggested for implementing experiments. We also reviewed several models that were suggested for implementing the extra-binomial variation or hyper-Poisson variability, and we noted how these models were generalized and further developed. The approaches that have been suggested for the overdispersion fall into two broad categories. The one is to develop a parametric model for it, and the other is to assume a particular relationship between the variance and the mean of the response variable and to derive a score test staistics for detecting the overdispersion. Recently, Dean(1992) derived a general score test statistics for detecting overdispersion from the exponential family.