• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.101-115
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• 2003

Lately there has been much theoretical and applied interest in linear models with non-normal heavy tailed error distributions. Starting Zellner(1976)'s study, many authors have explored the consequences of non-normality and heavy-tailed error distributions. We consider hierarchical models including selection models under a skewed heavy-tailed e..o. distribution proposed originally by Chen, Dey and Shao(1999) and Branco and Dey(2001) with Dirichlet process prior(Ferguson, 1973) in order to use a meta-analysis. A general calss of skewed elliptical distribution is reviewed and developed. Also, we consider the detail computational scheme under skew normal and skew t distribution using MCMC method. Finally, we introduce one example from Johnson(1993)'s real data and apply our proposed methodology.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.3
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• pp.195-202
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• 2015

NHPP software reliability models for failure analysis can have, in the literature, exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, finite failure NHPP models that assuming the expected value of the defect and infinite failures NHPP models that repairing software failure point in time reflects the situation, were presented for comparing property. Commonly used in the field of software reliability based on exponential power distribution software reliability model finite failures and infinite failures were presented for comparison problem. As a result, finite fault model is effectively infinite fault models, respectively. The parameters estimation using maximum likelihood estimation was conducted. In this research, software developers to identify software failure property some extent be able to help is considered.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.36 no.1
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• pp.20-36
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• 2024

In this study, probabilistic models for the wave- and lifting forces were derived directly from long-term in-situ wave data embedding the Korean marine environment characteristics varying from sea to sea based on the Three-Parameter Weibull distribution. Korean marine environment characteristics varying from sea to sea carved out their presence on the probability coefficients of probabilistic models for wave- and lifting forces. Energetic wave conditions along the southern coast of Korea distinguish themselves from the others with a relatively large scale coefficient, small location coefficient, and shape coefficient around 1.3. On the other hand, mild marine environment along the western coast has a small variability, leading to small scale-coefficient, large location coefficient and shape coefficient around 2.0. In the sea off Mokpo, near the boundary between the South- and West Seas, marine environment was characterized by small scale-coefficient, large location coefficient, and shape coefficient around 1.2, implying that marine environments characteristics of the South-and West Sea coexist in the sea off Mokpo.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.73-82
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• 2017

In this research, we tried to check the applicability of a Chow test to the linear models which are generated in the process of transportation planning or traffic flow analyses. The Chow test is a very popular statistical method which is being used to see if the coefficients from two separate linear regression models are equal or not. In order to prove the effectiveness of the Chow test, we found the linear relationships between speed and density under the situations such as driving in daytime and in nighttime on a rainy day. Based on the two months of Joong-Bu Expressway traffic data, we proved that the Chow test is useful to testify the similarity between two linear regression models. And this statistical tool seems to be able to have a very important role in traffic flow analysis or in transportation planning process. Finally, we expect the Chow test be implemented even to the non-linear regression models or to the multi-variate models.

• Environmental and Resource Economics Review
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• v.20 no.3
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• pp.595-628
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• 2011

This study investigates the issues and suggests reform measures in applying CV methods to the Korea Development Institute's (KDI's) Preliminary Feasibility Test (PFT) of public projects. Most public projects on culture, science and environment evaluated under the PFT system belong to the category of "nonstandard" projects whose outputs are non-marketed, and CV is currently the main tool used for their benefit estimation. A careful discussion and investigation is recommended for the selection of target population, payment vehicle, and number of payment times. Operating expert reviews, focus group interviews, and pre-tests is highly recommended to reduce the potential bias involved in the CV studies. A single or double bounded dichotomous choice format is the most popular design of questionnaire, but we identify several undissolved issues in designing and implementing the format. Some other forms of inducing WTPs may still deserve our consideration. Various specifications of the WTP function need to be tried and tested based on their stability, in particular. Employing a nonparametric approach is also recommended. Treatments of 0 or negative WTPs and protest bids are shown to be the most serious issues that affect the estimation results significantly. We review diverse measures of handling those issues and summarize their advantages and shortcomings.

• Asia pacific journal of information systems
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• v.13 no.4
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• pp.229-255
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• 2003

This article attempts to examine the well-known law that the increasing returns to scale(IRS) is effective in the Internet business. The effect of IRS is one of the hottest issues in the Internet business sector. Many cases and survey studies support the fact that the IRS phenomenon exists in the Internet business. Executives in Internet business generally give a deep trust on this theory. As the Internet business grows, however, the boundary of the business becomes widened and complicated. And each category of Internet business is characterized with a different business style and economic behavior. It may, therefore, be dangerous to accept that the phenomenon of IRS is applied to all areas of Internet business. For this reason, the research for the close look into the IRS phenomenon should provide significant implications for the managers in the Internet business industry. This article divides the internet business into four sub-areas, and analyzes the IRS phenomenon using AHP/DEA-based full ordering technique. Interpretations are given, based upon the research results, for each sub-area of Internet business, as a guideline of setting business strategies for practical managers.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1065-1075
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• 2008

For the model validation of credit rating models, Kolmogorov-Smirnov(K-S) statistic has been widely used as a testing method of discriminatory power from the probabilities of default for default and non-default. For the credit rating works, K-S statistics are to test two identical distribution functions which are partitioned from a distribution. In this paper under the assumption that the distribution is known, modified K-S statistic which is formulated by using known distributions is proposed and compared K-S statistic.

• Journal of Information Technology Applications and Management
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• v.27 no.6
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• pp.153-170
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• 2020

The Educational Restitution Rate is an important parameter that determines the quality of university education. This paper analyzed data from 148 private universities over the 10 years from 2009 to 2018 using data mining techniques in Korea. A significant causal relationship is detected in the fixed effect model as a result of the panel estimation. And the scale of faculty expansion and fund management, which are the university evaluation indicators, and the size of basic funds, respectively, have a positive effect on the ERR, which is within the confidence interval. In the analysis, the more private universities improve the tuition dependence rate, the more decisively positive affecting ERR. As a result of nonparametric regression estimation, when the faculty expansion ratio is reinforced, the effect of economies of scale is detected in some sections, the improvement of the tuition dependence rate, and the result value is generated through the improvement that results are derived at a certain point in time. We hope that the university based on this study can be a basic Indicators for the diagnosis of basic competencies and policy of student-centered education.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.593-605
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• 2019

Significant literature exists on the area under the ROC curve (AUC) and the volume under the ROC surface (VUS) which are statistical measures of the discriminant power of classification models. Whereas the partial AUC is restricted on the false positive rate, the two-way partial AUC is restricted on both the false positive rate and true positive rate, which could be more efficient and accurate than partial AUC. The two-way partial AUC was suggested as more efficient and accurate than the partial AUC. Partial VUS as well as the three-way partial VUS were also developed for the ROC surface. A proposed AUC is expressed in this paper with probability and integration using two truncated distribution functions restricted on both the false positive rate and true positive rate. It is also found that this AUC has a relation with the two-way partial AUC. The three-way partial VUS for the ROC surface is also related to the VUS using truncated distribution functions. These AUC and VUS are represented and estimated in terms of Mann-Whitney statistics. Their parametric and non-parametric estimation methods are explored based on normal distributions and random samples.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.