• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.171-180
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• 2016

Basel committee suggests using Value-at-Risk (VaR) and expected shortfall (ES) as a measurement for market risk. Various estimation methods of VaR and ES have been studied in the literature. This paper compares semi-parametric methods, such as conditional autoregressive value at risk (CAViaR) and conditional autoregressive expectile (CARE) methods, and a Gaussian quasi-maximum likelihood estimator (QMLE)-based method through back-testing methods. We use unconditional coverage (UC) and conditional coverage (CC) tests for VaR, and a bootstrap test for ES to check the adequacy. A real data analysis is conducted for S&P 500 index and Hyundai Motor Co. stock price index data sets.

• Korean Journal of Clinical Pharmacy
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• v.23 no.2
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• pp.89-96
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• 2013

This article aimed to introduce 'risk sharing' schemes for pharmaceuticals between drug manufacturers and healthcare payer. Published literature review was undertaken to summarize risk sharing concepts and collect information on existing scheme examples in other countries focusing on new anticancer drugs. Risk sharing schemes could be categorized into health outcomes-based and non-outcomes (financial) based ones. Outcome-based schemes could be broken down into performance-linked reimbursement and conditional coverage. Performance-linked reimbursement can be further broken into outcomes guarantee and pattern or process of care and conditional coverage included coverage with evidence development and conditional treatment continuation schemes. Non-outcome based schemes included market share and price volume at population level, and utilization caps and manufacturer funded treatment initiation at patient level. We reviewed the fifteen examples for anticancer drugs that risk sharing agreements in response to the inherent uncertainties and increased costs of eleven anticancer drugs. Of them, eight cases were coverage with evidence development schemes. The anticancer drugs except bevacizumab and cetuximab were all listed on the national health insurance formulary in Korea, with reimbursement criteria defined on the basis of approved indications and administrations. Risk sharing approach may be a useful tool to ensure values for drug expenditure, but there are a number of concerns such as high administration costs, lack of transparency and conflicts of interest, especially for performance-based health outcomes reimbursement schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3511-3532
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• 2019

Coverage-guided fuzzing is an efficient solution that has been widely used in software testing. By guiding fuzzers through the coverage information, seeds that generate new paths will be retained to continually increase the coverage. However, we observed that most samples follow the same few high-frequency paths. The seeds that exercise a high-frequency path are saved for the subsequent mutation process until the user terminates the test process, which directly affects the efficiency with which the low-frequency paths are tested. In this paper, we propose a fuzzing solution, ER-Fuzz, that truncates the recording of a high-frequency path to influence coverage. It utilizes a deep learning-based classifier to locate the high and low-frequency path transfer points; then, it instruments at the transfer position to promote the probability low-frequency transfer paths while eliminating subsequent variations of the high-frequency path seeds. We implemented a prototype of ER-Fuzz based on the popular fuzzer AFL and evaluated it on several applications. The experimental results show that ER-Fuzz improves the coverage of the original AFL method to different degrees. In terms of the number of crash discoveries, in the best case, ER-Fuzz found 115% more unique crashes than did AFL. In total, seven new bugs were found and new CVEs were assigned.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.909-916
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• 2010

A trend in software reliability engineering is to take into account the coverage growth behavior during testing. A coverage growth function that represents the coverage growth behavior is an essential factor in software reliability models. When multiple competitive coverage growth functions are available, there is a need for a criterion to select the best coverage growth functions. This paper proposes a selection criterion based on the prediction error. The conditional coverage growth function is introduced for predicting future coverage growth. Then the sum of the squares of the prediction error is defined and used for selecting the best coverage growth function.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.479-496
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• 2003

We investigate logit confidence intervals for the odds ratio based on the delta method. These intervals are constructed using pseudo-Bayes estimators. The Gart method and Agresti method smooth the observed counts toward the model of equiprobability and independence, respectively. We obtain better coverage probability by smoothing the observed counts toward the pseudo-Bayes estimators in 2$\times$2 table. We also improve legit confidence intervals in 2$\times$2$\times$K tables by generalizing these ideas. Utilizing pseudo-Bayes estimators, we obtain better coverage probability by smoothing the observed counts toward the conditional independence model, no three-factor interaction model and saturated model in 2$\times$2$\times$K tables.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.905-926
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• 1998

We propose a modified mid P-value method to reduce the conservativeness for the inference of conditional associations in three-way contingency tables. This improves the ordinary mfd P-value method. For $2{\times} 2${\times} K$ tables, we propose confidence intervals for an assumed common odds ratio based on inverting two separate one-sided tests using the modified mid P-value. Also, an alternative and usually even better ways of constructing intervals, based on Inverting a two-sided test, are presented. The actual probability of coverage of a 100($1-\alpha$)% confidence interval is centered about the nominal level, but the modified mid P-value approach gives actual coverage probability even closer to the nominal level than the ordinary mid P-value approach.

• KIISE Transactions on Computing Practices
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• v.22 no.4
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• pp.184-188
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• 2016

The cleaning robot is popularly used as a home appliance. The state-of-the-art cleaning robot can clean more efficiently by using information gathered from its sensor, which is difficult for low-price cleaning robots due to limitation in this aspect. In this paper, we suggested a method for the moving pattern of cleaning robot based on grammatical evolution. Optimized program is generated by using moving pattern grammar, which is defined by Backus-Naur form. In addition, conditional probability is used between each of the grammar elements during the program creation process. The proposed method is evaluated by robot simulation in order to verify its performance and further compare it with existing algorithms. The experiment results showed that the proposed method is better than the compared algorithms.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.277-289
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• 2003

We propose modified exact inferential methods in logistic regression model. Exact conditional distribution in logistic regression model is often highly discrete, and ordinary exact inference in logistic regression is conservative, because of the discreteness of the distribution. For the exact inference in logistic regression model we utilize the modified P-value. The modified P-value can not exceed the ordinary P-value, so the test of size $\alpha$ based on the modified P-value is less conservative. The modified exact confidence interval maintains at least a fixed confidence level but tends to be much narrower. The approach inverts results of a test with a modified P-value utilizing the test statistic and table probabilities in logistic regression model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.199-210
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• 1999

The number of car accident is Recently on the increase in Korea because of the explosive increase of cars, the poor road condition, the lack of safety facility, and others. The insurant with a accident has to decide whether receiving a insurance or not. In this paper, we represent a reasonable decision support material by calculating the approximate insurance fee based on the discount rate and premium additive rate, which is changed by the accident type and the accident expenditure. Practically, there is difference in the standard insurance rate and premium additive rate according to the accident type and the accident expenditure in Korea. The premium additive rate is assessed considering the number of accident, the pattern of accident, and the reason of accident for 3 years. In this paper, we represent a decision making method considering not only the first-time car accident but also the future car accident. For considering the repeated accident, we analyzed the real data accumulated until the year of 1996 from S Insurance Company, and estimated the probability density function between the first and the second-time accident, and executed the goodness of fit test using ARENA and STATISTICA software. Using this conditional PDF, we can calculate the insurance fee next 3 years and compare the insurance fee with the equivalent present value of cash flows. The program performing this analysis is represented, and written in VISUAL BASIC Language. We tried to suggest an accurate guideline for the insurant to decide the insurance coverage rationally, and tried to correct a wrong idea of dependence on the car insurance only by the amount of the accident expenditure. And we expect this study can generally be applied to many different accident types under the uncertain circumstances in our daily life.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.3
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• pp.437-444
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• 2014

In this paper, we introduce a Graph based Binary Code Execution Path Exploration Platform. In the graph, a node is defined as a conditional branch instruction, and an edge is defined as the other instructions. We implemented prototype of the proposed method and works well on real binary code. Experimental results show proposed method correctly explores execution path of target binary code. We expect our method can help Software Assurance, Secure Programming, and Malware Analysis more correct and efficient.