• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.47-57
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• 1996

This paper on planning constant accelerated life test is assumed that parameters for a lognormal life distribution are depended on changes of stresses. The proposed test plans are optimum in that they minimize the asymptotic variance of maximum likelihood estimator of a specified quantile at the design stress. The optimal amount of low stress level ${\xi}_{L}$ and optimal sample proportion ${\pi}$ to be allocated at low stress level are obtained when the ratio of scales at high stress level and design stress level is unknown.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.493-505
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• 2017

Maximum likelihood estimation (MLE) of the generalized extreme value distribution (GEVD) is known to sometimes over-estimate the positive value of the shape parameter for the small sample size. The maximum penalized likelihood estimation (MPLE) with Beta penalty function was proposed by some researchers to overcome this problem. But the determination of the hyperparameters (HP) in Beta penalty function is still an issue. This paper presents some data adaptive methods to select the HP of Beta penalty function in the MPLE framework. The idea is to let the data tell us what HP to use. For given data, the optimal HP is obtained from the minimum distance between the MLE and MPLE. A bootstrap-based method is also proposed. These methods are compared with existing approaches. The performance evaluation experiments for GEVD by Monte Carlo simulation show that the proposed methods work well for bias and mean squared error. The methods are applied to Blackstone river data and Korean heavy rainfall data to show better performance over MLE, the method of L-moments estimator, and existing MPLEs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.2
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• pp.534-539
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• 2010

In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future observations. There are three inherent steps to build classifiers: a significant gene selection, model selection and prediction assessment. In the paper, with a focus on prediction assessment, we normalize microarray data with quantile-normalization methods that adjust quartile of all slide equally and then design a system comparing several methods to estimate 'true' prediction error of a prediction model in the presence of feature selection and compare and analyze a prediction error of them. LOOCV generally performs very well with small MSE and bias, the split sample method and 2-fold CV perform with small sample size very pooly. For computationally burdensome analyses, 10-fold CV may be preferable to LOOCV.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.174-182
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• 2018

Given the ongoing debate in many aspects of finance, more attention may need to focus on corporate R&D expenditures. This study empirically tests financial determinants of R&D expenditures for NYSE-listed and KOSPI-listed firms. Three major hypotheses were postulated to test for corporate R&D outlay. First, proposed variables such as one-year lagged R&D expenditures, market value based leverage, profitability and cash holdings showed significant influence on corporate R&D costs for the sample firms. Moreover, financial factors inclusive of squared one-year lagged R&D expenditures, the interaction effect between one-lagged R&D expenditures and high-growth firm, non-debt tax shield, Tobin's q and a dummy variable to explain differences in accounting treatment between the U.S. and Korea, revealed significant differences between the two samples. Finally, in the conditional quantile regression (CQR) analysis for the R&D-related variables in relation to corporate growth rate, it was found that the NYSE-listed firms had a statistically significant linkage between growth potential and one-year lagged R&D expenditures at lower quantile levels. This study may shed new light on identifying financial factors affecting differences between the U.S. market (as an advanced market) and the Korean market (as an emerging market) regarding the optimal level of R&D investments for shareholders.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.161-170
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• 2014

It is important to estimate an appropriate quantile for design of hydraulic structure. For this purpose, it is necessary to find the appropriate probability distribution which can represent the sample data well. Probability plot correlation coefficient test as one of goodness-of-fit test, is recently developed and has been known as a simple and powerful method. In this study, probability plot correlation coefficient test statistics using the plotting position considering the coefficients of skewness for the GEV distribution is derived, and represented by the regression equation. Monte-Carlo method is also performed to compare the rejection power between each method. As the results, the probability plot correlation coefficient test which is derived in this study is better than the others. In particular, when sample size is small and distribution has the shape parameter, rejection power of probability plot correlation coefficient test considering the coefficients of skewness is bigger than the others.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.129-138
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• 2019

Ammunition is an integral element of a weapon systems and in calculating fighting strength. The Korea Army utilizes the basic load (B/L) concept to supply ammunition smoothly. The required supply rate (RSR) is the basis of a B/L that is estimated from real combat data that includes a troop's mission and operation terrain. The current RSR is based on Korean War data and the sample mean has some problems in applications to modern combat. Therefore, this study used Korea Combat Training Center (KCTC) data that is similar to real combat to estimate rifle ammunition RSR. We used a quantile of truncated Weibull distribution to estimate rifle ammunition RSR considering that rifle ammunition consumption data in KCTC is truncated. As a result, we obtained a rifle ammunition RSR which covers most ammunition consumption by reflecting the individual consumption of rifle ammunition.

• Journal of Korea Water Resources Association
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• v.44 no.2
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• pp.85-96
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• 2011

Probability plotting position is generally used for the graphical analysis of the annual maximum quantile and the estimation of exceedance probability to display the fitness between sample and an appropriate probability distribution. In addition, it is used to apply a specific goodness of fit test. Plotting position formula to define the probability plotting position has been studied in many researches. Especially, the GEV distribution which is an important probability distribution to analyze the frequency of hydrologic data was popular. In this study, the theoretical reduced variates are derived using the mean value of order statistics to derived an appropriate plotting position formula for the GEV distribution. In addition, various forms of plotting position formula considering various sample sizes and coefficients of skewness related with shape parameters are applied. The parameters of plotting position formulas are estimated using the genetic algorithm. The accuracy of derived plotting position formula is estimated by the errors between the theoretical reduced variates and those by various plotting position formulas including the derived ones in this study. As a result, the errors by derived plotting position formula is the smallest at the range of shape parameter with -0.25~0.10.