• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.99-112
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• 2016

Asymmetric jump-diffusion models are effectively used to model the dynamic behavior of asset prices with abrupt asymmetric upward and downward changes. However, the estimation of their extension to the multivariate asymmetric jump-diffusion model has been hampered by the analytically intractable likelihood function. This article confronts the problem using a data augmentation method and proposes a new Bayesian method for a multivariate asymmetric Laplace jump-diffusion model. Unlike the previous models, the proposed model is rich enough to incorporate all possible correlated jumps as well as mention individual and common jumps. The proposed model and methodology are illustrated with a simulation study and applied to daily returns for the KOSPI, S&P500, and Nikkei225 indices data from January 2005 to September 2015.

• The Korean Journal of Applied Statistics
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• v.15 no.1
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• pp.73-84
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• 2002

Linear regression models with inequality constraints on the coefficients are frequently used in economic models due to sign or order constraints on the coefficients. In this paper, we propose a Bayesian approach to selecting significant explanatory variables in linear regression models with inequality constraints on the coefficients. Bayesian variable selection requires computation of posterior probability of each candidate model. We propose a method which computes all the necessary posterior model probabilities simultaneously. In specific, we obtain posterior samples form the most general model via Gibbs sampling algorithm (Gelfand and Smith, 1990) and compute the posterior probabilities by using the samples. A real example is given to illustrate the method.

• Journal of Korean Ophthalmic Optics Society
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• v.5 no.1
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• pp.173-180
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• 2000

To determine the effect of base curve and diameter of soft contact lenses on the fitting under the various corneal curvature, the model eyes which was made of either stainless steel or ordinary wood, were used as the substitutes for human eyes. The evaluations of fit of the soft contact lenses on both wood model eyes and human eyes were found to be very similar to each other. All the contact lenses except very thin ones became flat after fit on the stainless steel model eyes because the model eye could not preserve enough moisture to hold the edge of contact lenses on the steel ball's surface. The relationships between the base curves of contact lenses and radii of cornea for the optimum (normal) fit were measured as follows : corneal curvature (C.C)<7.6 mm : base curve(B.C) 8.4 mm, C.C 7.6~7.8 mm : B.C 8.4~8.5 mm. C.C 7.8~8.1 mm : B.C 8.6 mm. It is concluded that larger base curve is required for the eyes which have abnormal bulge on its cornea. It is found that very thin soft contact lenses can be easily twisted or folded regardless of moisture content when they were fit on the relatively dry eyes(corneas).

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.115-127
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• 2023

Multinomial probit model is a popular model for multiclass classification and choice model. Markov chain Monte Carlo (MCMC) method is widely used for estimating multinomial probit model, but its computational cost is high. However, it is well known that variational Bayesian approximation is more computationally efficient than MCMC, because it uses subsets of samples. In this study, we describe multinomial probit model with Gaussian process classification and how to employ variational Bayesian approximation on the model. This study also compares the results of variational Bayesian multinomial probit model to the results of naive Bayes, K-nearest neighbors and support vector machine for the UCI mice protein expression level data.

• Korean Journal of Optics and Photonics
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• v.18 no.2
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• pp.167-170
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• 2007

In this study, clinical data for emmetopia in young Koreans was taken in order to design the finite schematic eye, which had optical properties of real eyes including spherical aberration, astigmatism, field curvature and distortion. Furthermore, the crystalline lens with GRIN medium was optically analyzed, and the finite schematic eye with the GRIN crystalline lens was designed.

• Korean Journal of Optics and Photonics
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• v.14 no.3
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• pp.236-243
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• 2003

We discussed the design of lens module schematic eyes equivalent to finite model eyes, which are used to model the human eye based on spherical aberration and Stiles-Crowford effect. The optical system for head mounted display (HMD) is designed and evaluated using lens module schematic eyes. In addition to a compact HMD system, an optical system with high Performance is required. To satisfy these requirements, we used diffractive optical elements and aspheric surfaces so that the color and mono-chromatic aberrations were corrected. The optical system for HMD is composed of 0.47 inch micro-display of SVGA grade with 480,000 pixels, a plastic hybrid lens for the virtual image, and the lens module schematic eyes. The designed optical system fulfills the current specifications of HMD: such as, EFL of 31.25 mm, FOV of 24H$\times$18V$\times$30D degrees, and overall length of 59.1 mm. As a result, we could design an optical system useful for HMD; the system is expected to be comfortable while the user wears it.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.195-202
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• 2006

본 연구에서는 보다 효과적인 기업부도예측을 위하여, 동계적 방법과 인공지능 방법을 결합한 통합모형을 제시하였다. 이를 위하여 통계적인 모형 중에서 가장 널리 활용되고 있는 다변량 판별분석, 로지스틱 회귀분석과 인공 지능적인 방법으로서 최근 널리 사용되고 있는 인공신경망, 규칙유도기법, 베이지안 망의 5가지 방법론을 통합한 Voting with Performance & Weights from ANN(WP-ANN) 통합모형을 제시하였다. 실험결과, 본 연구에서 제안한 WP-ANN 통합모형은 다변량 판별분석, 로지스탁 회귀분석, 인공신경망, 규칙유도기법, 베이지안 망 등의 단일모형과 비교한 결과 가장 예측정확성이 유수한 것으로 나타났다. 따라서 본 연구를 통해 기업부도예측에 있어서 WP-ANN 통합모형이 기존의 모형들에 비해 우수한 예측정확성을 나타냄을 알 수 있었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.512-516
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• 2005

우리나라에서는 이제까지 치수사업의 타당성 분석시 경제성 분석에 너무 치우쳐 사업에 대한 의사결정과정이 단편적으로 진행되어 왔다. 이러한 문제를 개선하고자, 본 연구는 AHP(Analytic Hierarchy Process)과 효용함수(utility function)를 이용하여 대안의 수립, 평가, 선정, 우선순위 결정에 이르는 치수사업 의사결정과정을 모형화 하였다. 모형의 적용결과, 최선대안의 선정시 경제성 기준이 여전히 큰 비중을 차지하고 있었으나 단위 사업안의 투자우선순위 결정에는 위험성, 지속가능성 기준의 영향도 상당부분 있었다. 그러나 대안들 간에 변별력을 높이기 위해서는 경제성 이외 기준들의 속성에서 공간적 정밀도를 높이는 추가연구는 필요한 것으로 판단되었다. 본 연구는 치수사업 계획이 유역 내 단위사업안들로 조합된 대안들의 평가를 통해 이루어져야 한다는 점을 보여주고 있으며, 향후 점차로 유역단위의 치수계획이 정립됨에 따라 본 연구를 바탕으로 보다 구체적인 연구가 수행되리라 기대된다.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.119-133
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• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.

• Economic Analysis
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• v.26 no.1
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• pp.84-132
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• 2020

A central bank infers market expectations of future yields based on yield curves. The central bank needs to precisely understand the changes in market expectations of future yields in order to have a more effective monetary policy. This need explains why a range of models have attempted to produce yield curves and market expectations that are as accurate as possible. Alongside the development of bond markets, the interconnectedness between them and macroeconomic factors has deepened, and this has rendered understanding of what macroeconomic variables affect yield curves even more important. However, the existence of various theories about determinants of yields inevitably means that previous studies have applied different macroeconomics variables when estimating yield curves. This indicates model uncertainties and naturally poses a question: Which model better estimates yield curves? Put differently, which variables should be applied to better estimate yield curves? This study employs the Dynamic Nelson-Siegel Model and takes the Bayesian approach to variable selection in order to ensure precision in estimating yield curves and market expectations of future yields. Bayesian variable selection may be an effective estimation method because it is expected to alleviate problems arising from a priori selection of the key variables comprising a model, and because it is a comprehensive approach that efficiently reflects model uncertainties in estimations. A comparison of Bayesian variable selection with the models of previous studies finds that the question of which macroeconomic variables are applied to a model has considerable impact on market expectations of future yields. This shows that model uncertainties exert great influence on the resultant estimates, and that it is reasonable to reflect model uncertainties in the estimation. Those implications are underscored by the superior forecasting performance of Bayesian variable selection models over those models used in previous studies. Therefore, the use of a Bayesian variable selection model is advisable in estimating yield curves and market expectations of yield curves with greater exactitude in consideration of the impact of model uncertainties on the estimation.