• Fire Science and Engineering
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• v.23 no.2
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• pp.36-40
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• 2009

This paper presents research about the development of electrical fire cause classification in order to improve the reliability of electrical fire statistics and to collect electrical fires data efficiently. The incorrect and biased knowledge for electrical fires changed the classification of certain types of fires, from non-electrical to electrical. It is convenient and required to develop the standardized form that makes, in the assessment of the cause of electrical fires, the fire investigators directly ticking the appropriate box on the fire report form or making an assessment of a text description. In this study, newly developed electrical fire cause classification structure, which is well-defined hierarchical structure so that there are not any relationship or overlap between cause categories, is suggested. Also the suggested classification structure can be used for electrical fire investigation and statistics, which minimizes the mistake that diagnose non-electrical fires into electrical ones.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.65-81
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• 1998

Simultaneous equation models, increasingly used in many detailed analyses, tend to get larger and more sophisticated to describe the structure of the study area to be close to the actual situations. In setting up such a system of equations, statistical results and simulation performance of the model as a whole may be meaningless and unrepresentative of the real world due to a structural instability that is built into the model when the equations are combined and solved simultaneously. Even though the use and subsequent analysis of an unstable system are likely to mislead us, most of the studies that take the simultaneous equation approaches neglect such a serious problem. Thus it is necessary to illustrate how to check the stability problem and apply to the actual model, then investigate how such as analysis is able to provide useful information about the structural characteristics of the model from the dynamic viewpoint.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.759-770
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• 2009

A problem of separating signals from noises is considered, when they are randomly mixed in the observation. It is assumed that the noise follows a Gaussian distribution and the signal follows a Gamma distribution, thus the underlying distribution of an observation will be a mixture of Gaussian and Gamma distributions. The parameters of the mixture model will be estimated from the EM algorithm. Then the signals and noises will be classified by a fixed threshold approach based on multiple testing using positive false discovery rate and Bayes error. The proposed method is applied to a real optical emission spectroscopy data for the quantitative analysis of inclusions. A simulation is carried out to compare the performance with the existing method using 3 sigma rule.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1115-1124
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• 2010

Normal approximation methods under the null hypothesis of no difference are frequently used to test the two independent proportions in non-inferiority trials. However, these tests are not appropriate under the null hypothesis of non-zero difference. We review the likelihood score methods proposed by Miettinen and Nurminen, Farrington and Manning, and Gart and Nam and compare the performance of these tests. The simulation study shows that the likelihood score tests under the null hypothesis of non-zero difference have better performance at a Type I error and power than usual normal approximation methods.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.103-114
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• 2011

Current series for testing stock market cointegrations tend to be restricted to analyzing the relations between stock market prices and may not be able to understand the whole picture of the variations in the stock market system. The nature of the variations in the stock prices, between the countries that experienced economic crisis and those did not, are different for a certain period of time, and accordingly excluding the potentially important variables in the stock market system causes statistical bias. This study considers domestic foreign exchange markets and financial markets in testing for the cointegrating relations of the stock prices in Korea and major investing countries. The results demonstrate the possibility of specification errors unless those markets are included in the statistical modeling process.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.297-310
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• 2002

Liang and Zeger proposed generalized estimating equations(GEE) for analyzing repeated data which is discrete or continuous. GEE model can be extended to model for repeated categorical data and its estimator has asymptotic multivariate normal distribution in large sample sizes. But GEE is based on large sample asymptotic theory. In this paper, we study the properties of GEE estimators for repeated ordinal data in small sample sizes. We generate ordinal repeated measurements for two groups using two methods. Through Monte Carlo simulation studies we investigate the empirical type 1 error rates, powers, relative efficiencies of the GEE estimators, the effect of unequal sample size of two groups, and the performance of variance estimators for polytomous ordinal response variables, especially in small sample sizes.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.343-352
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• 2000

In this study. the author proposes a nonhierarchical clustering method. called the "Double K-Means Clustering", which performs clustering of multivariate observations with the following algorithm: Step I: Carry out the ordinary K-means clmitering and obtain k temporary clusters with sizes $n_1$,... , $n_k$, centroids $c_$1,..., $c_k$ and pooled covariance matrix S. $\bullet$ Step II-I: Allocate the observation x, to the cluster F if it satisfies ..... where N is the total number of observations, for -i = 1, . ,N. $\bullet$ Step II-2: Update cluster sizes $n_1$,... , $n_k$, centroids $c_$1,..., $c_k$ and pooled covariance matrix S. $\bullet$ Step II-3: Repeat Steps II-I and II-2 until the change becomes negligible. The double K-means clustering is nearly "optimal" under the mixture of k multivariate normal distributions with the common covariance matrix. Also, it is nearly affine invariant, with the data-analytic implication that variable standardizations are not that required. The method is numerically demonstrated on Fisher's iris data.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.833-842
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• 2014

Non-inferiority trials indicate whether the effect of an experimental treatment is not worse than an active control. Chen et al. (2006) and Kang (2010) proposed a test method for non-inferiority trials using confidence intervals. In this paper, we suggest a new nonparametric method using a confidence interval based on Wilcoxon rank-sum test and Hodges-Lehmann estimator of active control. A Monte-Carlo simulation study compares the type I error and the power of the proposed method with previous methods.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.595-610
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• 2013

Testing of order-restricted alternative hypothesis in $2{\times}k$ contingency tables can be applied to various fields of medicine, sociology, and business administration. Most testing methods have been developed based on a large sample theory. In the case of a small sample size or unbalanced sample size, the Type I error rate of the testing method (based on a large sample theory) is very different from the target point of 5%. In this paper, the exact testing method is introduced in regards to the testing of an order-restricted alternative hypothesis in categorical data (particularly if a small sample size or extreme unbalanced data). Power and exact p-value are calculated, respectively.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.605-608
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• 2008

We have studied statistically about quality difference of Blast furnace slag Type Ⅲ that affects carbonation of concrete. According to KS F 2563 blast furnace slag was classified 3 types. Type Ⅲ blast furnace slag(specific surface area is $4000cm^2/g$) from different providers with Type A, the B and the C. The statistical technique was applied to exclude error of engineering judgement. T test and F test were used among 3 groups to investigate statistical meaning. The effect which on the quality of blast furnace slag type Ⅲ that affects carbonation of concrete is significant.