• Journal of the Korean Statistical Society
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• v.32 no.4
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• pp.425-448
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• 2003

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. We consider hierarchical models including selection models under a skewed heavy tailed error distribution proposed originally by Chen et al. (1999) and Branco and Dey (2001). These rich classes of models combine the information of independent studies, allowing investigation of variability both between and within studies, and incorporate weight function. Here, the testing for the skewness parameter is discussed. The score test statistic for such a test can be shown to be expressed as the posterior expectations. Also, we consider the detail computational scheme under skewed normal and skewed Student-t distribution using MCMC method. Finally, we introduce one example from Johnson (1993)'s real data and apply our proposed methodology. We investigate sensitivity of our results under different skewed errors and under different prior distributions.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.851-858
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• 2012

We consider a bivariate Poisson regression model to analyze discrete count data when two dependent variables are present. We estimate the regression coefficients as sociated with several safety countermeasures. We use Markov chain and Monte Carlo techniques to execute some computations. A simulation and real data analysis are performed to demonstrate model fitting performances of the proposed model.

• Journal of Korean Society for Quality Management
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• v.37 no.4
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• pp.16-22
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• 2009

The inflection S-shaped software reliability growth model (SRGM) proposed by Ohba(1984) is one of the most commonly used models and has been discussed by many authors. The main purpose of this paper is to estimate the parameters of Ohba's SRGM within the Bayesian framework by applying the Markov chain Monte Carlo techniques. While the maximum likelihood estimates for these parameters are well known, the Bayesian method for the inflection S-shaped SRGM have not been discussed in the literature. The proposed methods can be quite flexible depending on the choice of prior distributions for the parameters of interests. We also compare the Bayesian methods with the maximum likelihood method numerically based on the real data.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.393-406
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• 2015

We examine how to model mortality risk using the adaptation of the mean-reverting processes for the two factor model proposed by Cairns et al. (2006b). Mortality improvements have been recently observed in some countries such as United Kingdom; therefore, we assume long-run mortality converges towards a trend at some unknown time and the mean-reverting processes could therefore be an appropriate stochastic model. We estimate the parameters of the two-factor model incorporated with mean-reverting processes by a Metropolis-Hastings algorithm to fit United Kingdom mortality data from 1991 to 2015. We forecast the evolution of the mortality from 2014 to 2040 based on the estimation results in order to evaluate the issue price of a longevity bond of 25 years maturity. As an application, we propose a method to quantify the speed of mortality improvement by the average mean reverting times of the processes.

• Journal of Korea Water Resources Association
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• v.48 no.1
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• pp.37-44
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• 2015

A Bayesian nonstationary probability rainfall estimation model using the Grid method is developed. A hierarchical Bayesian framework is consisted with prior and hyper-prior distributions associated with parameters of the Gumbel distribution which is selected for rainfall extreme data. In this study, the Grid method is adopted instead of the Matropolis Hastings algorithm for random number generation since it has advantage that it can provide a thorough sampling of parameter space. This method is good for situations where the best-fit parameter values are not easily inferred a priori, and where there is a high probability of false minima. The developed model was applied to estimated target year probability rainfall using hourly rainfall data of Seoul station from 1973 to 2012. Results demonstrated that the target year estimate using nonstationary assumption is about 5~8% larger than the estimate using stationary assumption.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.405-419
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• 2022

Flood frequency analysis commonly used to design the hydraulic structures to minimize flood damage includes uncertainty. Therefore, the most appropriate design flood within a uncertainty should be selected in the final stage of a hydraulic structure, but related studies were rarely carried out. The total expected cost function introduced into the flood frequency analysis is a new approach for determining the optimal design flood. This procedure has been used as UNCODE (UNcertainty COmpliant DEsign), but the application has not yet been introduced in South Korea. This study introduced the mathematical procedure of UNCODE and calculated the optimal design flood using the annual maximum inflow of hydroelectric dams located in the Bukhan River system and results were compared with that of the existing flood frequency. The parameter uncertainty was considered in the total expected cost function using the Gumbel and the GEV distribution, and the Metropolis-Hastings algorithm was used to sample the parameters. In this study, cost function and damage function were assumed to be a first-order linear function. It was found that the medians of the optimal design flood for 4 Hydroelectric dams, 2 probability distributions, and 2 return periods were calculated to be somewhat larger than the design flood by the existing flood frequency analysis. In the future, it is needed to develop the practical approximated procedure to UNCODE.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.873-880
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• 2021

The purpose of this study is to investigate the impact of internal control on credit risk of joint stock commercial banks in Vietnam from 2007 to 2018. Furthermore, we specify bank-specific characteristics and macroeconomic conditions, and analyze how these factors affect credit risk of banks: the number of board members, the number of board members with banking or finance background as ratio of total board members, loans to total assets ratio, loans to deposit ratio, the number of days between the year-end and the publication of the financial statements, and the use of top four auditing firms proxy for five elements of internal control. By using the dataset of 30 Vietnamese joint stock commercial banks and Bayesian linear regression via Random-walk Metropolis Hastings algorithm, the results of this study show that five elements of internal control have a impact on bank credit risk, namely, control environment, risk assessment, control activities, information and communication, and monitoring activities. For factors of banks' characteristics, bank size and financial leverage have a negative impact on banks' credit risk, and bank age has a positive effect. For macroeconomic factors, inflation has a positive impact and economic growth has a negative impact on banks' credit risk.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.94-99
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• 2015

With the recent machine learning paradigm of using nonparametric Bayesian statistics and statistical inference based on random sampling, the Dirichlet distribution finds many uses in a variety of graphical models. It is a multivariate generalization of the gamma distribution and is defined on a continuous (K-1)-simplex. This paper presents a sampling method for a Dirichlet distribution for the problem of dividing an integer X into a sequence of K integers which sum to X. The target samples in our problem are all positive integer vectors when multiplied by a given X. They must be sampled from the correspondingly gridded simplex. In this paper we develop a Markov Chain Monte Carlo (MCMC) proposal distribution for the neighborhood grid points on the simplex and then present the complete algorithm based on the Metropolis-Hastings algorithm. The proposed algorithm can be used for the Markov model, HMM, and Semi-Markov model for accurate state-duration modeling. It can also be used for the Gamma-Dirichlet HMM to model q the global-local duration distributions.

• Smart Structures and Systems
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• v.24 no.5
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• pp.631-639
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• 2019

Finite element analysis is one of the important methods to study the structural performance. Due to the simplification, discretization and error of structural parameters, numerical model errors always exist. Besides, structural characteristics may also change because of material aging, structural damage, etc., making the initial finite element model cannot simulate the operational response of the structure accurately. Based on Bayesian methods, the initial model can be updated to obtain a more accurate numerical model. This paper presents the work on the field test, modal identification and model updating of a Chinese reinforced concrete pagoda. Based on the ambient vibration test, the acceleration response of the structure under operational environment was collected. The first six translational modes of the structure were identified by the enhanced frequency domain decomposition method. The initial finite element model of the pagoda was established, and the elastic modulus of columns, beams and slabs were selected as model parameters to be updated. Assuming the error between the measured mode and the calculated one follows a Gaussian distribution, the posterior probability density function (PDF) of the parameter to be updated is obtained and the uncertainty is quantitatively evaluated based on the Bayesian statistical theory and the Metropolis-Hastings algorithm, and then the optimal values of model parameters can be obtained. The results show that the difference between the calculated frequency of the finite element model and the measured one is reduced, and the modal correlation of the mode shape is improved. The updated numerical model can be used to evaluate the safety of the structure as a benchmark model for structural health monitoring (SHM).

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.203-213
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• 2020

The self-controlled case series (SCCS) study measures the relative risk of exposure to exposure period by setting the non-exposure period of the patient as the control period without a separate control group. This method minimizes the bias that occurs when selecting a control group and is often used to measure the risk of adverse events after taking a drug. This study used SCCS to examine the increased risk of side effects when two or more drugs are used in combination. A conditional Poisson model is assumed and analyzed for drug interaction between the narcotic analgesic, tramadol and multi-frequency combination drugs. Bayesian inference is used to solve the overfitting problem of MLE and the normal or Laplace prior distributions are used to measure the sensitivity of the prior distribution.