• Journal of the Korean Data and Information Science Society
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• 제26권4호
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• pp.813-826
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• 2015

This research deals with an estimation method for kinetic reaction model. The kinetic reaction model is a model to explain spread or changing process based on interaction between species on the Biochemical area. This model can be applied to a model for disease spreading as well as a model for system Biology. In the search, we assumed that the spread of species is stochastic and we construct the reaction model based on stochastic movement. We utilized Gillespie algorithm in order to construct likelihood function. We introduced a Bayesian estimation method using Markov chain Monte Carlo methods that produces more stable results. We applied the Bayesian estimation method to the Lotka-Volterra model and gene transcription model and had more stable estimation results.

• The Korean Journal of Applied Statistics
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• 제14권1호
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• pp.161-175
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• 2001

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. Hierarchical models including selection models are introduced and shown to be useful in such Bayesian meta-analysis. Semiparametric hierarchical models are proposed using the Dirichlet process prior. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierachical selection model with including unknown weight function and use Markov chain Monte Carlo methods to develop inference for the parameters of interest. Using Bayesian method, this model is used on a meta-analysis of twelve studies comparing the effectiveness of two different types of flouride, in preventing cavities. Clinical informative prior is assumed. Summaries and plots of model parameters are analyzed to address questions of interest.

• The Korean Journal of Applied Statistics
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• 제26권3호
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• pp.453-470
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• 2013

In this paper, we consider a Bayesian analysis of the dose-effect relationship of cadmium to evaluate a benchmark dose(BMD). For this purpose, two dose-response curves commonly used in the toxicity study are fitted based on Bayesian methods to the data collected from the scientific literature on cadmium toxicity. Specifically, Bayesian meta-analysis and hierarchical modeling build an overall dose-effect relationship that use a piecewise linear model and Hill model, where the inter-study heterogeneity and inter-individual variability of dose and effect such as gender, age and ethnicity are accounted. Estimation of the unknown parameters is made by using a Markov chain Monte Carlo algorithm based user-friendly software WinBUGS. Benchmark dose estimates are evaluated for various cut-offs and compared with different tested subpopulations with with gender, age and ethnicity based on these two Bayesian hierarchical models.

• The Korean Journal of Applied Statistics
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• 제8권2호
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• pp.55-64
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• 1995

Three-level Skip-lot sampling plan split by two stages (Split2-SkSP0 is proposed by modifying multi-level skip-lot sampling plan proposed by Choi(1993), which has normal and terrace inspections on the first and the second stages, respectively. The plan is designed to work more higher level inspections when the quality of the submitted products are good, otherwise, return to the normal or the terrace inspection as fast as possible. Also, the formula of the operating characteristic function for the split skip-lot sampling plan is derived using the Markov chain approach. Also, operating characteristic properties of the proposed plans are studied and graphically compared with those of the multi-level skip-lot sampling plans.

• The Korean Journal of Applied Statistics
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• 제27권6호
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• pp.959-973
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• 2014

Asset pricing models that account for asymmetric volatility in asset prices have been recently proposed. This article presents an efficient Bayesian method to analyze asset-pricing models. The method is developed by devising a partially collapsed Gibbs sampler that capitalizes on the functional incompatibility of conditional distributions without complicating the updates of model components. The proposed method is illustrated using simulated data and applied to daily S&P 500 data observed from September 1980 to August 2014.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2004년도 학술발표회
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• pp.748-752
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• 2004

근래 환경의 중요성이 크게 인식되면서 하천의 수질관리에 대해서도 많은 연구가 진행되고 있다. 오염된 하천의 수질을 개선하기 위한 방법 중에 배출 오염원을 삭감시키는 환경기초시설이나 댐과 같은 저수구조물에서 양질의 용수를 방류하여 희석시킴으로써 하천의 수질오염을 개선할 수 있다. 본 연구에서는 하천수질을 개선시키는 과정에서 희석을 위한 저수지의 방류량을 필요유량으로 간주하였으며 일정 수질농도를 지속적으로 지니고 있는 자연하천의 유입량과 이때 어느 대상지점의 목표수질등급을 만족시킬 수 있는 저수구조물에서의 인위적 조정이 가능한 필요유량의 관계를 연구하였다. 연구대상 지역은 금강 본류와 본류로 유입하는 갑천유역을 대상으로 하였다. 즉, 갑천의 확률갈수유량을 3등급과 4등급으로 가정하고, 이들 유량이 금강본류로 유입되련, 금강본류와 미호천합류 바로 전의 금강하류지점 목표수질을 2등급이라 하였을 때, 이를 만족할 수 있는 금강본류의 필요유량을 산정하고자 하였다. 이때 갑천유역의 일강우량자료는 기존 Markov 연쇄의 상태모형을 개선해 모의하였고, 강우-유출 모형으로는 NWS-PC모형을 이용하였는데 컴플렉스 혼합 진화기법으로 매개변수들을 보정하여 확률갈수유량을 산정하였다. 다음에 QUAL2E를 이용하여 목표수질을 만족하는 희석 목적의 유랑을 구하여 필요유량으로 간주하였다. 결과를 살펴보면, 유입되는 갑천의 확률갈수유량들에 대하여 대청조정지댐에서 방류량이 목표수질을 만족시키기 위해서는 $0.5\~2.5$배정도 더 유입되어야 함을 알 수 있었다.

• The Korean Journal of Applied Statistics
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• 제23권1호
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• pp.189-196
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• 2010

It is never an easy task to physically randomize the sequence of cards. For instance, US 1970 draft lottery resulted in a social turmoil since the outcome sequence of 366 birthday numbers showed a significant relationship with the input order (Wikipedia, "Draft Lottery 1969", Retrieved 2009/05/01). We are motivated by Laplace's 1825 book titled Philosophical Essay on Probabilities that says "Suppose that the numbers 1, 2, ..., 100 are placed, according to their natural ordering, in an urn, and suppose further that, after having shaken the urn, to shuffle the numbers, one draws one number. It is clear that if the shuffling has been properly done, each number will have the same chance of being drawn. But if we fear that there are small differences between them depending on the order in which the numbers were put into the urn, we can decrease these differences considerably by placing these numbers in a second urn in the order in which they are drawn from the first urn, and then shaking the second urn to shuffle the numbers. These differences, already imperceptible in the second urn, would be diminished more and more by using a third urn, a fourth urn, &c." (translated by Andrew 1. Dale, 1995, Springer. pp. 35-36). Laplace foresaw what would happen to us in 150 years later, and, even more, suggested the possible tool to handle the problem. But he did omit the detailed arguments for the solution. Thus we would like to write the supplement in modern terms for Laplace in this research note. We formulate the problem with a lottery box model, to which Markov chain theory can be applied. By applying Markov chains repeatedly, one expects the uniform distribution on k states as stationary distribution. Additionally, we show that the probability of even-number of successes in binomial distribution with trials and the success probability $\theta$ approaches to 0.5, as n increases to infinity. Our theory is illustrated to the cases of truncated geometric distribution and the US 1970 draft lottery.

• KSCE Journal of Civil and Environmental Engineering Research
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• 제14권3호
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• pp.509-521
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• 1994

This study is an effort to develop computer simulation model that produce precipitation patterns from stochastic model. A stochastic model is formulated for the process of daily precipitation with considering the sequences of wet and dry days and the precipitation amounts on wet days. This study consists of 2 papers and the process of precipitation occurrence is modelled by an alternate renewal process (ARP) in paper (I). In the ARP model for the precipitation occurrence, four discrete distributions, used to fit the wet and dry spells, were as follows; truncated binomial distribution (TBD), truncated Poisson distribution (TPD), truncated negative binomial distribution (TNBD), logarithmic series distribution (LSD). In companion paper (II) the process of occurrence is developed by Markov chain. The amounts of precipitation, given that precipitation has occurred, are described by a Gamma. Pearson Type-III, Extremal Type-III, and 3 parameter Weibull distribution. Daily precipitation series model consists of two models, A-Wand A-G model, by combining the process of precipitation occurrence and a continuous probability distribution on the precipitation of wet days. To evaluate the performance of the simulation model, output from the model was compared with historical data of 7 stations in the Nakdong and Seomjin river basin. The results of paper (1) show that it is possible to design a model for the synthetic generation of IX)int precipitation patterns.

• The Korean Journal of Applied Statistics
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• 제24권4호
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• pp.677-693
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• 2011

In this paper, we consider Bayesian approaches to zero inflated Poisson model, one of the popular models to analyze zero inflated count data. To generate posterior samples, we deal with a Markov Chain Monte Carlo method using a Gibbs sampler and an exact sampling method using an Inverse Bayes Formula(IBF). Posterior sampling algorithms using two methods are compared, and a convergence checking for a Gibbs sampler is discussed, in particular using posterior samples from IBF sampling. Based on these sampling methods, a real data analysis is performed for Trajan data (Marin et al., 1993) and our results are compared with existing Trajan data analysis. We also discuss model selection issues for Trajan data between the Poisson model and zero inflated Poisson model using various criteria. In addition, we complement the previous work by Rodrigues (2003) via further data analysis using a hierarchical Bayesian model.

• The Korean Journal of Applied Statistics
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• 제29권1호
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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.