• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.563-571
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• 2001

In this paper, we study selecting a transformation so that the transformed variable is nearly symmetrically distributed. The large sample properties of an M-estimator of transformation parameter that is obtained by minimizing the integrated square of the imaginary part of the empirical characteristic function are investigated when a random sample is selected from some unspecified distribution. According to influence function calculations and Monte Carlo simulations, these estimates are less sensitive, than the normal model maximum likelihood estimates, to a few outliers.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.349-361
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• 2009

In this paper, we derive the approximate maximum likelihood estimators of the shape parameter and the scale parameter in a Weibull distribution under multiply Type-II censoring by the approximate maximum likelihood estimation method. We develop three modified empirical distribution function type tests for the Weibull distribution based on multiply Type-II censored samples. We also propose modified normalized sample Lorenz curve plot and new test statistic.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.543-549
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• 2008

Inspired by using a robust loss function in the support vector machine regression to control training error and the idea of robust template matching with M-estimator, Chen (2004) applies M-estimator techniques to gaussian radial basis functions and form a new class of robust kernels for the support vector machines. We are specially interested in the shape of the Huber's M-estimator in this context and propose a way to find the shape parameter of the Huber's M-estimating function. For simplicity, only the two-class classification problem is considered.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.213-220
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• 2006

Since Beran (1977) developed the minimum Hellinger distance estimation, this method has been a popular topic in the field of robust estimation. In the process of defining a distance, a kernel density estimator has been widely used as a density estimator. In this article, however, we show that a combination of a kernel density estimator and an empirical density could result a smaller bias of the minimum Hellinger distance estimator than using just a kernel density estimator for a location parameter.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1551-1559
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• 2013

In this paper, we analyze the Korean Won/Japanese 100 Yen exchange rate data based on the ARMA-GARCH model, and perform the test for detecting the parameter changes. As a test statistics, we employ the cumulative sum (CUSUM) test for ARMA-GARCH model, which is introduced by Lee and Song (2008). Our empirical analysis indicates that the KRW/JPY exchange rate series experienced several parameter changes during the period from January 2000 to December 2012, which leads to a fitting of AR-IGARCH model to the whole series.

• Journal of the Korean Geotechnical Society
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• v.17 no.6
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• pp.25-36
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• 2001

The purpose of this paper is to discuss the effects of soil properties such as liquid limit, water content, etc. on the compression index and to propose the empirical equation of compression index far regional clay and to verify the application Back Propagation Neural Network(BPNN). The compression index values obtained from laboratory tests are in the range of 0.01 to 3.06 for clay soils sampled in eleven regions. As the compare with the results of laboratory test and the predicted compression index value from the proposed empirical equations, the results of empirical equations including single soil parameter have a possibility to be overestimated. Also, the results of empirical equations including multiple soil parameters closed to the measured value more than that of empirical equations including single soil parameter, but the standard error for measured value obtained larger than 0.05. For these reasons, the empirical equations including single or multiple soil parameters proposed base on the results of laboratory test and the determination coefficient is up to 0.89. The result of BPNN shows that correlation coefficient and standard error between test and neural network result is larger than 0.925 and smaller than 0.0196, which means high correlativity, respectively. Especially, the estimated result by neural network, using only three parameters such as natural water content, dry unit weight and in-situ void ratio among various factors is available to the estimation of compression index and the correlation coefficient is 0.974. This result verified the possibility that if BPNN use, the compression index can be predicted by the parameters, which obtained from simplex field test.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.431-440
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• 2013

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.

• Journal of Korea Water Resources Association
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• v.46 no.6
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• pp.643-653
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• 2013

The most important procedure in frequency analysis is to determine the appropriate probability distribution and to estimate quantiles for a given return period. To perform the frequency analysis, the goodness-of-fit tests should be carried out for judging fitness between obtained data from empirical probability distribution and assumed probability distribution. The previous goodness-of-fit could not consider enough extreme events from the recent climate change. In this study, the critical values of the modified Anderson-Darling test statistics were derived for 3-parameter Weibull distribution and power test was performed to evaluate the performance of the suggested test. Finally, this method was applied to 50 sites in South Korea. The result shows that the power of modified Anderson-Darling test has better than other existing goodness-of-fit tests. Thus, modified Anderson-Darling test will be able to act as a reference of goodness-of-fit test for 3-parameter Weibull model.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.1151-1155
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• 2010

Frost depth is one of important factors to design roadway structure, and it can be estimated with numerical simulation on thermal distribution through subgrade soils. Thermal conductivity is a key parameter for accurate prediction on thermal distribution, but there are few studies on thermal conductivity of subgrade soils in Korea. Thermal conductivity can be affected by several factors such as dry density, moisture content, and saturation degree based on previous researches. Two empirical equations to estimate thermal conductivity are applied to access the accuracy of these equations with experimental data. Results indicate that the equation can be used to estimate thermal conductivity with proper quartz fraction.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.321-327
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• 2013

Bayesian and empirical Bayesian methods have become quite popular in the theory and practice of statistics. However, the objective is to often produce an ensemble of parameter estimates as well as to produce the histogram of the estimates. For example, in insurance pricing, the accurate point estimates of risk for each group is necessary and also proper dispersion estimation should be considered. Well-known Bayes estimates (which is the posterior means under quadratic loss) are underdispersed as an estimate of the histogram of parameters. The adjustment of Bayes estimates to correct this problem is known as constrained Bayes estimators, which are matching the first two empirical moments. In this paper, we propose a way to apply the constrained Bayes estimators in insurance pricing, which is required to estimate accurately both location and dispersion. Also, the benefit of the constrained Bayes estimates will be discussed by analyzing real insurance accident data.