• Communications of the Korean Mathematical Society
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• v.21 no.2
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• pp.363-374
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• 2006

A new bivariate beta distribution based on the Appell function of the third kind is introduced. Various representations are derived for its product moments, marginal densities, marginal moments, conditional densities and conditional moments. The method of maximum likelihood is used to derive the associated estimation procedure as well as the Fisher information matrix.

• Journal of the Korean Statistical Society
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• v.33 no.1
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• pp.107-128
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• 2004

Bivariate Laplace distributions for which both marginal distributions and Laplace are discussed. Three kinds of bivariate Laplace distributions which are extended bivariate exponential distributions of Gumbel (1960) are introduced in this paper. These symmetrical distributions are compared with asymmetrical distributions of Kotz et al. (2000). Their probability density functions, cumulative distribution functions are derived. Conditional skewnesses and kurtoses are also defined. Their correlation coefficients are calculated and compared with others. We proposed bivariate random vector generating methods whose distributions are bivariate Laplace. With sample means and medians obtained from generated random vectors, variance and covariance matrices of means and medians are calculated and discussed with those of bivariate normal distribution.

• 한국연소학회:학술대회논문집
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• 2000.12a
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• pp.24-32
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• 2000

A brief introduction is given on the conditional moment closure model for turbulent nonpremixed combustion. It is based on the transport equations derived through a rigorous mathematical procedure for the conditionally averaged quantities and appropriate modeling forms for conditional scalar dissipation rate, conditional mean velocity and reaction rate. Examples are given for prediction of NO and OH in bluffbody flames, soot distribution in jet flames and autoignition of a methane/ethane jet to predict the ignition delay with respect to initial temperature, pressure and fuel composition. Conditional averaging may also be a powerful modeling concept in other approaches involved in turbulent combustion problems in various different regimes.

• Journal of the Korean Society of Combustion
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• v.5 no.2
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• pp.9-17
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• 2000

A brief introduction is given on the conditional moment closure model for turbulent nonpremixed combustion. It is based on the transport equations derived through a rigorous mathematical procedure for the conditionally averaged quantities and appropriate modeling forms for conditional scalar dissipation rate, conditional mean velocity and reaction rate. Examples are given for prediction of NO and OR in bluffbody flames, soot distribution in jet flames and autoignition of a methane/ethane jet to predict the ignition delay with respect to initial temperature, pressure and fuel composition. Conditional averaging may also be a powerful modeling concept in other approaches involved in turbulent combustion problems in various different regimes.

• The KIPS Transactions:PartC
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• v.16C no.2
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• pp.199-208
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• 2009

Recently,IP-based broadcasting systems,such as Mobile-TV and IP-TV, have been widely deployed. These systems require a security system to allow only authorized subscribers access to broadcasting services. We analyzed the Conditional Access System, which is a security system used in the IP-based Pay-TV systems. A weakness of the system is that it does not scale well when the system experiences frequent membership changes. In this paper, we propose a group key distribution protocol which overcomes the scalability problem by reducing communication and computation overheads without loss of security strength. Our experimental results show that computation delay of the proposed protocol is smaller than one of the Conditional Access System. This is attributed to the fact that the proposed protocol replaces expensive encryption and decryption with relatively inexpensive arithmetic operations. In addition, the proposed protocol can help to set up a secure channel between a server and a client with the minimum additional overhead.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.589-596
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• 2011

The conditional autoregressive value at risk (CAViaR) model is useful for risk management, which does not require the assumption that the conditional distribution does not vary over time but the volatility does. But it does not provide volatility forecasts, which are needed for several important applications such as option pricing and portfolio management. For a variety of probability distributions, it is known that there is a constant relationship between the standard deviation and the distance between symmetric quantiles in the tails of the distribution. This inspires us to use a support vector quantile regression (SVQR) for volatility forecasts with the distance between CAViaR forecasts of symmetric quantiles. Simulated example and real example are provided to indicate the usefulness of proposed forecasting method for volatility.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.29-43
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• 2012

Financial data such as stock index returns and exchange rates have the properties of heavy tail and asymmetry compared to normal distribution. When we estimate VaR using the GARCH model (with the conditional return distribution of normal) it shows the tendency of the lower estimation and clustering in the losses over the estimated VaR. In this paper, we argue that this problem can be resolved through the adaptation of the unbounded Johnson distribution as that of the condition return. We also compare this model with the GARCH with the conditional return distribution of normal and student-t. Using the losses exceed the ex-ante VaR, estimates, we check the validity of the GARCH models through the failure proportion test and the clustering test. We nd that the GARCH model with conditional return distribution of unbounded Johnson provides an appropriate estimation of the VaR and does not occur the clustering of violations.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.503-512
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• 2006

In this paper, the Inverse Weibull distribution parameters have been estimated using a new estimation technique based on the non-parametric kernel density function that introduced as an alternative and reliable technique for estimation in life testing models. This technique will require bootstrapping from a set of sample observations for constructing the density functions of pivotal quantities and thus the confidence intervals for the distribution parameters. The performances of this technique have been studied comparing to the conditional inference on the basis of the mean lengths and the covering percentage of the confidence intervals, via Monte Carlo simulations. The simulation results indicated the robustness of the proposed method that yield reasonably accurate inferences even with fewer bootstrap replications and it is easy to be used than the conditional approach. Finally, a numerical example is given to illustrate the densities and the inferential methods developed in this paper.

• East Asian Economic Review
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• v.27 no.1
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• pp.33-60
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• 2023

This paper analyzes the entire distribution of stock market returns/volatility in five emerging markets (ASEAN5) and figures out the conditional distribution of the CHI_EPU index. The aim is to examine the impact of CHI_EPU on the stock returns/volatility density of ASEAN5 markets. It also examined whether changes in CHI_EPU explain returns at higher or lower points (abnormal returns). This paper models the behaviour of stock returns from March 2011 to June 2018 using a non-parametric conditional density estimation approach. The results indicate that CHI_EPU diminishes stock returns and augments volatility in ASEAN5 markets, except for Malaysia, where it affects stock returns positively. The possible reason for this positive impact is that EPU is not the leading factor reducing Malaysian stock returns; but, other forces, such as dependency on other countries' stock markets and global factors, may have a positive impact on stock returns (Bachmann and Bayer, 2013). Thus, the risk of simultaneous investment in Chinese and ASEAN5 stock markets, except Malaysia, is high. Further, the degree of this influence intensifies at extreme high/low intervals (positive/negative tails). The findings of this study have significant implications for investors, policymakers, market agents, and analysts of ASEAN5.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.203-213
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• 2017

We study estimation and inference of joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. We consider a class of time-varying transformation models and combine the two marginal models using Gaussian copulas to estimate the joint models. Our models and estimation method can be applied in many situations where the conditional mean-based models are inadequate. Gaussian copulas combined with time-varying transformation models may allow convenient and easy-to-interpret modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.