• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1065-1074
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• 2011

During several decades, many researchers in the field of finance have studied Value at Risk (VaR) to measure the market risk. VaR indicates the worst loss over a target horizon such that there is a low, pre-specified probability that the actual loss will be larger (Jorion, 2006, p.106). In this paper, we compare conditional copula method with two conventional VaR forecasting methods based on simple moving average and exponentially weighted moving average for measuring the risk of the portfolio, consisting of two domestic stock indices. Through real data analysis, we conclude that the conditional copula method can improve the accuracy of portfolio VaR forecasting in the presence of high kurtosis and strong correlation in the data.

• School Mathematics
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• v.9 no.1
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• pp.29-44
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• 2007

The aim of statistics education is to enhance statistical thinking. Variability is the key components of statistical thinking. The research has been reviewed preceding research about variability of data. Proceeding from what has been considered above, this research developed learning materials that investigated the concept of variability as it relates to Freudenthal's context by having students sort a particular context. The research is executed the case study evidently aimed at Junior High School 3rd Grade Student's Understanding of Variability. The study of variability in data can be an important start to reach a testing of statistical hypothesis; students reduce data and draw graphs by relating probability distribution to relative frequency and normal distribution. Thus, this study offers basic materials into developing both contents and methods of education need to consider with this sense of purpose held by students to achieve this goal.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.321-330
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• 2016

Run expectancy (RE) is the mean number of runs scored from a specific base runner/outs situation of an inning to the end of the inning. Win expectancy (WE) is the probability that a particular team will win the game at a specific game state such as half-inning, score difference, outs, and/or runners on base. In this paper, we derive RE and WE for the Korea Baseball Organization (KBO) League based on six-year data from 2007 to 2012 using a Markov chain model.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.281-290
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• 2007

Linear estimator, a weighted sum of the sample observation, is commonly adopted to estimate the finite population parameters such as population totals in survey sampling. The weight for a sampled unit is often constructed by multiplying the base weight, which is the inverse of the first-order inclusion probability, by an adjustment term that takes into account of the auxiliary information obtained throughout the population. The linear estimator using the weight adjustment is often more efficient than the one using only the bare weight, but its valiance estimation is more complicated. We discuss variance estimation for a general class of weight-adjusted estimator. By identifying that the weight-adjusted estimator can be viewed as a function of estimated nuisance parameters, where the nuisance parameters were used to incorporate the auxiliary information, we derive a linearization of the weight-adjusted estimator using a Taylor expansion. The method proposed here is quite general and can be applied to wide class of the weight-adjusted estimators. Some examples and results from a simulation study are presented.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.615-620
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• 2008

In engineering experiments, 'Sometimes Pooling Rules' to remove insignificant terms from the model has been implemented to increase the power of detecting the small size of main effects when the preliminary test of higher order interaction effects declare to be insignificant. In this note, we review the sometimes pooling rules in the literature and also study the probability of the length of 95% confidence interval of ${\mu}_1-{\mu}_2$ of the comparison of two independent samples being shorter than that of the paired comparison at the various level of significance ${\alpha}_1$ of the preliminary test and the insufficient number of blocks n in [2,13], given the block effects being pooled to the error term. This study supports that the sometimes pooling results in the power improvement of the main effects.

• Journal of the Korean GEO-environmental Society
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• v.17 no.12
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• pp.55-64
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• 2016

In this paper bearing capacity and safety margin of shallow foundation on weathered soil ground against shear failure by using current design method of allowable stress design (ASD), load resistance factor design (LRFD) based on reliability analysis and partial safety factor design (PSFD) in Eurocode were estimated and compared to each other. Results of the plate loading test used in construction and design were collected and analysis of probability statistics on soil parameters affecting the bearing capacity of shallow foundation was performed to quantify the uncertainty of them and to investigate the resistance bias factor and covalence of ultimate bearing capacity. For the typical sections of shallow foundation in domestic field as examples, reliability index was obtained by reliability analysis (FORM) and the sensitivity analysis on soil parameters of probability variables was performed to investigate the effect of probability variable on shear failure. From stability analysis for these sections by ASD, LRFD with the target reiability index corresponding to the safety factor used in ASD and PSDF, safety margins were estimated respectively and compared.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.10 no.16
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• pp.101-106
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• 1987

Statistical control charts are useful tools to monitor and control the manufacturing processes and are widely used in most Korean industries. Many Korean companies, however, do not always obtain desired results from the traditional control charts by Shewhart such as the $\bar{X}$-chart, $\bar{X}$-chart, $\bar{X}$-chart, etc. This is partly because the quality charterstics of the process are not distributed normally but are skewed due to the intermittent production, small lot size, etc. In Shewhart $\bar{X}$-chart. which is the most widely used one in Kora, such skewed distributions make the plots to be inclined below or above the central line or outside the control limits although no assignable causes can be found. To overcome such shortcomings in nonnormally distributed processes, a distribution-free type of confidence interval can be used, which should be based on order statistics. This thesis is concerned with the design of control chart based on a sample median which is easy to use in practical situation and therefore properties for nonnormal distributions may be easily analyzed. Control limits and central lines are given for the more famous nonnormal distributions, such as Gamma, Beta, Lognormal, Weibull, Pareto, Truncated-normal distributions. Robustness of the proposed median control chart is compared with that of the $\bar{X}$-chart; the former tends to be superior to the latter as the probability distribution of the process becomes more skewed. The average run length to detect the assignable cause is also compared when the process has a Normal or a Gamma distribution for which the properties of X are easy to verify, the proposed chart is slightly worse than the $\bar{X}$-chart for the normally distributed product but much better for Gamma-distributed products. Average Run Lengths of the other distributions are also computed. To use the proposed control chart, the probability distribution of the process should be known or estimated. If it is not possible, the results of comparison of the robustness force us to use the proposed median control chart based oh a normal distribution. To estimate the distribution of the process, Sturge's formula is used to graph the histogram and the method of probability plotting, $\chi$$^2$-goodness of fit test and Kolmogorov-Smirnov test, are discussed with real case examples. A comparison of the proposed median chart and the $\bar{X}$ chart was also performed with these examples and the median chart turned out to be superior to the $\bar{X}$-chart.

• Journal of Animal Science and Technology
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• v.47 no.4
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• pp.491-500
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• 2005

To test applicability to the Hanwoo traceability system, twenty microsatellite markers were selected and analyzed. MSA, CERVUS, FSTAT, GENEPOP, API_CALC and PHYLIP software was employed serially to estimate heterozygosity, polymorphic information content, F-statistics, identity probability, exclusion probability and genetic distance. Eleven microsatellite markers(TGLA53, TGLA227, ETH185, TGLA122, BM4305, INRA23, ILSTS013, BMS1747, BM2113, BL1009, and ETH3) were selected based on their high heterozygosity values. Identity probability using these markers is one hundred times higher than when using StockMakersTM of Applied Biosystems. This indicates the selected microsatellite markers are appropriate and effective for use in the Hanwoo traceability system. Additionally, estimates of DA genetic distance and pairwise-FST can be utilized to identify genetic relationships between adjacent farms.

• Communications of Mathematical Education
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• v.25 no.1
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• pp.261-288
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• 2011

We examined the effects of pair assistant collaborative learning on academic achievement of the 2nd year middle school students in the three subjects such as the Probability, Properties of Figures and Similarities of Figures. In order to carry out this study, we selected 2 classes of 2nd year students in a girls middle school in the Fall semester of 2009 and set up the experiment group and comparison group by the result of academic achievement tests given in the end of the Spring semester of 2009. Pair assistant collaborative learning was adopted for students in problem solving 2 or 3 times per a week in the experiment group and each academic achievement was given at the end of each subject in both groups. Also, we had a final survey to find out students' attitude to this collaborative learning. The achievement and survey were analysed by statistical methods. We conclude that our pair assistant collaborative learning was effective in Probability and Similarities of Figures Units. According to the result of survey, this collaborative learning brought about an opportunity to promote students' community spirit through reflecting each one's role in the group.

• Journal of the Korean Professional Engineers Association
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• v.20 no.3
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• pp.15-25
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• 1987

Statistical control charts are useful tools to monitor and control the manufacturing processes and are widely used in most Korean industries. Many Korean companies, however, do not always obtain desired results from the traditional control charts by Shewhart such as the X-chart, X-chart, X-chart, etc. This is partly because the quality charterstics of the process are not distributed normally but are skewed due to the intermittent production, small lot size, etc. In Shewhart X-chart, which is the most widely used one in Korea, such skewed distributions make the plots to be inclined below or above the central line or outside the control limits although no assignable causes can be found. To overcome such shortcomings in nonnormally distributed processes, a distribution-free type of confidence interval can be used, which should be based on order statistics. This thesis is concerned with the design of control chart based on a sample median which is easy to use in practical situation and therefore properties for nonnormal distributions may be easily analyzed. Control limits and central lines are given for tile more famous nonnormal distributions, such as Gamma, Beta, Lognormal, Weibull, Pareto, Truncated-normal distributions. Robustness of the proposed median control chart is compared with that of the X-chart, the former tends to be superior to the latter as the probability distribution of the process becomes more skewed. The average run length to detect the assignable cause is also compared when the process has a Normal or a Gamma distribution for which the properties of X are easy to verify, the proposed chart is slightly worse than the X-chart for the normally distributed product but much better for Gamma-distributed products. Average Run Lengths of the other distributions are also computed. To use the proposed control chart, the probability distribution of the process should be known or estimated. If it is not possible, the results of comparison of the robustness force us to use the proposed median control chart based on a normal distribution. To estimate the distribution of the process, Sturge's formula is used to graph the histogram and the method of probability plotting, $X^2$-goodness of fit test and Kolmogorov-Smirnov test, are discussed with real case examples. A comparison of the propose4 median chart and the X chart was also performed with these examples and the median chart turned out to be superior to the X-chart.