• 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.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.221-235
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• 1999

Let be $X_1$,...,$X_p$, $p\geq2$ independent random variables where each $X_i$ has a gamma distribution with $\textit{k}_i$ and $\theta_i$ The problem is to simultaneously estimate $\textit{p}$ gamma parameters $\theta_i$ and $\theta_i{^-1}$ under entropy loss where the parameters are believed priori. Hierarch ical Bayes(HB) and empirical Bayes(EB) estimators are investigated. And a preference of HB estimator over EB estimator is shown using Gibbs sampler(Gelfand and Smith 1990). Finally computer simulation is studied to compute the risk percentage improvements of the HB estimator and the estimator of Dey Ghosh and Srinivasan(1987) compared to UMVUE estimator of $\theta^{-1}$.

• Journal of the Korean Data and Information Science Society
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• v.2
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• pp.23-39
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• 1991

The maximum likelihood estimators and Bayes estimators of the parameters and reliability function for the two-parameter Weibull distribution under the type-II progressively censoring schemes are derived when a shape parameter is known and unknown, respectively. Efficiencies for above estimators are also compared each other in terms of the mean square errors, and Bayes risk sensitivities of the Bayes estimators are investigated.

• Journal of Distribution Science
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• v.18 no.9
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• pp.31-43
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• 2020

Purpose: This study examined a comprehensive model for assessing the success probability of electric vehicle (EV) commercialization in the Korean market. The study identified three risks associated with successful commercialization which were technology, social, policy, environmental, and consumer risk. Research design, methodology: The assessment of the riskiness was represented by a Bayes belief network, where the probability of success at each stage is conditioned on the outcome of the preceding stage. Probability of success in each stage is either dependent on input (i.e., investment) or external factors (i.e., air quality). Initial input stages were defined as the levels of investment in product R&D, battery technology, production facilities and battery charging facilities. Results: Reasonable levels of investment were obtained by expert opinion from industry experts. Also, a survey was carried out with 78 experts consisting of automaker engineers, managers working at EV parts manufacturers, and automobile industry researchers in government think tanks to obtain the conditional probability distributions. Conclusion: The output of the model was the likelihood of success - expressed as the probability of market acceptance - that depended on the various input values. A model is a useful tool for understanding the EV industry as a whole and explaining the likely ramifications of different investment levels.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.191-200
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• 1993

An asymptotic estimation problem of the unknown mean is studied under a general loss function. The proof of this result is based on the asymptotic expansion of the risk function. Also conditions for second order admissibility and minimaxity of a class of estimators depending only on the sample mean are established.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.297-306
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• 2018

Cardiovascular disease (CVD) is the leading cause of death worldwide and has a high mortality rate after onset; therefore, the CVD management requires the development of treatment plans and the prediction of prevalence rates. In our study, age, income, education level, marriage status, diabetes, and obesity were identified as risk factors for CVD. Using these 6 factors, we proposed a nomogram based on a $na{\ddot{i}}ve$ Bayesian classifier model for CVD. The attributes for each factor were assigned point values between -100 and 100 by Bayes' theorem, and the negative or positive attributes for CVD were represented to the values. Additionally, the prevalence rate can be calculated even in cases with some missing attribute values. A receiver operation characteristic (ROC) curve and calibration plot verified the nomogram. Consequently, when the attribute values for these risk factors are known, the prevalence rate for CVD can be predicted using the proposed nomogram based on a $na{\ddot{i}}ve$ Bayesian classifier model.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.471-488
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• 2018

In this paper, we propose extending proportional hazards frailty models to allow a discrete distribution for the frailty variable. Having zero frailty can be interpreted as being immune or cured. Thus, we develop a new survival model induced by discrete frailty with zero-inflated power series distribution, which can account for overdispersion. This proposal also allows for a realistic description of non-risk individuals, since individuals cured due to intrinsic factors (immunes) are modeled by a deterministic fraction of zero-risk while those cured due to an intervention are modeled by a random fraction. We put the proposed model in a Bayesian framework and use a Markov chain Monte Carlo algorithm for the computation of posterior distribution. A simulation study is conducted to assess the proposed model and the computation algorithm. We also discuss model selection based on pseudo-Bayes factors as well as developing case influence diagnostics for the joint posterior distribution through ${\psi}-divergence$ measures. The motivating cutaneous melanoma data is analyzed for illustration purposes.

• Genomics & Informatics
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• v.1 no.2
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• pp.80-86
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• 2003

Human Papillomavirus (HPV) infection is known as the main factor for cervical cancer which is a leading cause of cancer deaths in women worldwide. Because there are more than 100 types in HPV, it is critical to discriminate the HPVs related with cervical cancer from those not related with it. In this paper, the risk type of HPVs using their textual explanation. The important issue in this problem is to distinguish false negatives from false positives. That is, we must find high-risk HPVs as many as possible though we may miss some low-risk HPVs. For this purpose, the AdaCost, a cost-sensitive learner is adopted to consider different costs between training examples. The experimental results on the HPV sequence database show that the consideration of costs gives higher performance. The improvement in F-score is higher than that of the accuracy, which implies that the number of high-risk HPVs found is increased.

• Journal of Korean Society for Quality Management
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• v.26 no.4
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• pp.265-276
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• 1998

A commercial nuclear power station contains at least two emergency diesel generators(EDG) to control the risk of severe core damage during station blackout accidnets. Therefore, thereliability of the EDG's to start and load-run on demand must be maintained at a sufficiently high level. Until now, a simple assessment of start and load-run success rates was used to calculate the EDG reliability. However, this method has been found to contain many defects. Recently, the work of Martz et al.(1996) proposed the use of the Bayes estimator to find EDG reliability. Shim(1996) proposed a confidence interval for the Bayes estimator, compare the above two methods. In this paper, we introduce the notion of "Composite Reliablility" to estimate the reliability of nuclear-power plant EDG, and using practical examples, illustrate which method is more a, pp.opriate in our situation.situation.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.477-486
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• 2021

This study was conducted to detect metabolic syndrome risk factors and gender difference in adults. 18,616 cases of adults are collected by Korea Health and Nutrition Examination Study from 2016 to 2019. Using 4 types of machine Learning(Logistic Regression, Decision Tree, Naïve Bayes, Random Forest) to predict Metabolic Syndrome. The results showed that the Random Forest was superior to other methods in men and women. In both of participants, BMI, diet(fat, vitamin C, vitamin A, protein, energy intake), number of underlying chronic disease and age were the upper importance. In women, education level, menarche age, menopause was additional upper importance and age, number of underlying chronic disease were more powerful importance than men. Future study have to verify various strategy to prevent metabolic syndrome.