• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.147-157
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• 2014

Women's roles have changed substantially in economically developed countries; subsequently, the ratio of female drivers has also increased. In such countries, there has been considerable interest in assessing gender differences in vehicle accident risks and reasons to explain the gender differences. This study investigates the gender differences in vehicle accident risk based on 500,000 drivers randomly selected from a population sample. A Heckman model is used for accident damage and a negative binomial model is used for the accident frequency. Empirical results show that male drivers are 8.3% riskier than female drivers in terms of accident damage; however, female drivers are 113% risker than male drivers in term of accident frequency. We can implement more practical policies to reduce vehicle accidents if we can understand the reasons for the gender differences.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.711-721
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• 2021

In this paper, we propose the method of adjusting thresholds using impurity indices in classification analysis on imbalanced data. Suppose the minority category is Positive and the majority category is Negative for the imbalanced binomial data. When categories are determined based on the commonly used 0.5 basis, the specificity tends to be high in unbalanced data while the sensitivity is relatively low. Increasing sensitivity is important when proper classification of objects in minority categories is relatively important. We explore how to increase sensitivity through adjusting thresholds. Existing studies have adjusted thresholds based on measures such as G-Mean and F1-score, but in this paper, we propose a method to select optimal thresholds using the chi-square statistic of CHAID, the Gini index of CART, and the entropy of C4.5. We also introduce how to get a possible unique value when multiple optimal thresholds are obtained. Empirical analysis shows what improvements have been made compared to the results based on 0.5 through classification performance metrics.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2002.05b
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• pp.181-198
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• 2002

This article describes a methodology for evaluating huge aerospace R&D investments using the real options pricing method. Option pricing has been proposed as a useful approach for modeling investment in R&D. Two important features of R&D investments are that an R&D project takes time to complete and that the outcome of R&D investments is highly uncertain. This makes the analysis of R&D investments difficult. Traditional tools for project evaluation, like IRR or the NPV, are inadequate for coping with the high uncertainty. Hence, In this article I propose a log-transformed binomal lattice method, and it will show that option pricing might be an adequate framework for evaluating such types of aerospace investments.

• International Journal of Highway Engineering
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• v.18 no.4
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• pp.93-102
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• 2016

PURPOSES : There are many recently constructed roundabouts in Jeollabuk-do province. This study analyzed how roundabouts reduce the risk of accidents and improve safety in the province. METHODS : This study analyzed safety improvement at roundabouts by using an accident prediction model that uses an Empirical Bayes method based on negative binomial distribution. RESULTS : The results of our analysis model showed that the total number of accidents decreased from 130 to 51. Roundabouts also decreased casualties; the number of casualties decreased from 7 to 0 and the seriously wounded from 87 to 16. The effectiveness of accident reduction as analyzed by the accident prediction model with the Empirical Bayes method was 60%. CONCLUSIONS : The construction of roundabouts can bring about a reduction in the number of accidents and casualties, and make intersections safer.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.3
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• pp.99-109
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• 2009

Negative binomial yield model for semiconductor manufacturing consists of two parameters which are the average number of defects per die and the clustering parameter. Estimating the clustering parameter is quite complex because the parameter has not clear closed form. In this paper, a Bayesian approach using Markov Chain Monte Carlo is proposed to estimate the clustering parameter. To find an appropriate estimation method for the clustering parameter, two typical estimators, the method of moments estimator and the maximum likelihood estimator, and the proposed Bayesian estimator are compared with respect to the mean absolute deviation between the real yield and the estimated yield. Experimental results show that both the proposed Bayesian estimator and the maximum likelihood estimator have excellent performance and the choice of method depends on the purpose of use.

• Korean Journal of Materials Research
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• v.13 no.10
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• pp.658-662
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• 2003

In this study, we have tried to find the optimized design variables of the matching layer which is important part of thickness mode ultrasonic transducer and finally reach the conclusion that the electrical property of piezo-element must be under consideration when the optimized acoustic impedance is estimated. Proper expression of the effective impedance of front load at free resonant frequency(: $Z_{f}$ $^{(0)}$ /) has been induced by introducing the principle of binomial multilayer transformer and gradient based numerical method is utilized to find the most acceptable value of $Z_{ f}$/$^{(0)}$ . Optimized point of acoustic impedance can be calculated directly from $Z_{f}$ $^{(0)}$ using some simple formula which we propose. We also verify our result by both numerical and experimental method and get a good enhancement especially it concern to the bandwidth of ultrasonic transducer.

• Journal of Construction Engineering and Project Management
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• v.3 no.1
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• pp.10-13
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• 2013

Determining on a particular construction method is typically decided in the initial phases of a project. However, changing conditions during actual construction may require a different method or technology to be employed. Providing an option for project managers to change construction provides flexibility that can increase value to the overall project. This research provides the ability to modify construction methods as a real option, which allows its value to be modeled. The research also formalizes a way to integrate a binomial lattice model with the Earned Value Method's S-curve. The integrated model provides a decision support tool that planners can use to determine whether to exercise the option depending on the status metrics provided by EVM.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.129-132
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• 2013

Determining on a particular construction method is typically decided in the initial phases of a project. However, changing conditions during actual construction may require a different method or technology to be employed. Providing an option for project managers to change construction provides flexibility that can increase value to the overall project. This research provides the ability to modify construction methods as a real option, which allows its value to be modeled. The research also formalizes a way to integrate a binomial lattice model with the Earned Value Method's S-curve. The integrated model provides a decision support tool that planners can use to determine whether to exercise the option depending on the status metrics provided by EVM.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.55-64
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• 2024

In this study, we consider the sample size determination problem for clustered count data with many zeros. In general, zero-inflated Poisson and binomial models are commonly used for zero-inflated data; however, in real data the assumptions that should be satisfied when using each model might be violated. We calculate the required sample size based on a discrete Weibull regression model that can handle both underdispersed and overdispersed data types. We use the Monte Carlo simulation to compute the required sample size. With our proposed method, a unified model with a low failure risk can be used to cope with the dispersed data type and handle data with many zeros, which appear in groups or clusters sharing a common variation source. A simulation study shows that our proposed method provides accurate results, revealing that the sample size is affected by the distribution skewness, covariance structure of covariates, and amount of zeros. We apply our method to the pancreas disorder length of the stay data collected from Western Australia.

• Journal of applied mathematics & informatics
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• v.42 no.1
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• pp.179-187
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• 2024

In the Quality Control and inspection processes, the use of attribute sampling strategies is crucial. In this study, we incorporate the neutrosophic fuzzy acceptance sampling plan method to present a fresh approach to attribute sampling plans. Utilizing the benefits of neutrosophic fuzzy sets, the proposed sampling plan method models and assesses the acceptance standards for attribute sampling. We compare the suggested method to already-in-use attribute sampling techniques plans with new attribute six sigma sampling techniques plan is proposed in order to verified its efficacy. The outcomes show the neutrosophic fuzzy acceptance sampling plan's superiority in terms of its capacity to manage uncertainties, account for ambiguity, and produce more precise quality evaluation outputs.