• The Korean Journal of Applied Statistics
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• v.21 no.5
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• pp.835-843
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• 2008

In this paper, we investigate a Bayesian inference for software reliability models based on mean value functions which take the form of the mixture of beta distribution functions. The posterior simulation via the Markov chain Monte Carlo approach is used to produce estimates of posterior properties. Its applicability is illustrated with two real data sets. We compute the predictive distribution and the marginal likelihood of various models to compare the performance of them. The model comparison results show that the model based on the beta-mixture performs better than other models.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.613-635
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• 2001

This paper proposes a skewed multivariate probit model for analyzing a correlated binary response data with covariates. The proposed model is formulated by introducing an asymmetric link based upon a skewed multivariate normal distribution. The model connected to the asymmetric multivariate link, allows for flexible modeling of the correlation structure among binary responses and straightforward interpretation of the parameters. However, complex likelihood function of the model prevents us from fitting and analyzing the model analytically. Simulation-based Bayesian inference methodologies are provided to overcome the problem. We examine the suggested methods through two data sets in order to demonstrate their performances.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.99-118
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• 2021

In this paper, we introduce an extended form of the inverse power Lomax model via Marshall-Olkin approach. We call it the Marshall-Olkin inverse power Lomax (MOIPL) distribution. The four- parameter MOIPL distribution is very flexible which contains some former and new models. Vital properties of the MOIPL distribution are affirmed. Maximum likelihood estimators and approximate confidence intervals are considered under Type I censored samples. Maximum likelihood estimates are evaluated according to simulation study. Bayesian estimators as well as Bayesian credible intervals under symmetric loss function are obtained via Markov chain Monte Carlo (MCMC) approach. Finally, the flexibility of the new model is analyzed by means of two real data sets. It is found that the MOIPL model provides closer fits than some other models based on the selected criteria.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.373-391
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• 2022

The distribution of observations in most econometric studies with spatial heterogeneity is skewed. Usually, a single transformation of the data is used to approximate normality and to model the transformed data with a normal assumption. This assumption is however not always appropriate due to the fact that panel data often exhibit non-normal characteristics. In this work, the normality assumption is relaxed in spatial mixed models, allowing for spatial heterogeneity. An inference procedure based on Bayesian mixed modeling is carried out with a multivariate skew-elliptical distribution, which includes the skew-t, skew-normal, student-t, and normal distributions as special cases. The methodology is illustrated through a simulation study and according to the empirical literature, we fit our models to non-life insurance consumption observed between 1998 and 2002 across a spatial panel of 103 Italian provinces in order to determine its determinants. Analyzing the posterior distribution of some parameters and comparing various model comparison criteria indicate the proposed model to be superior to conventional ones.

• ETRI Journal
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• v.45 no.3
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• pp.394-403
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• 2023

Direct tracking problem of moving noncircular sources for multiple arrays is investigated in this study. Here, we propose an improved unscented particle filter (I-UPF) direct tracking method, which combines system proportional symmetry unscented particle filter and Markov Chain Monte Carlo (MCMC) algorithm. Noncircular sources can extend the dimension of sources matrix, and the direct tracking accuracy is improved. This method uses multiple arrays to receive sources. Firstly, set up a direct tracking model through consecutive time and Doppler information. Subsequently, based on the improved unscented particle filter algorithm, the proposed tracking model is to improve the direct tracking accuracy and reduce computational complexity. Simulation results show that the proposed improved unscented particle filter algorithm for noncircular sources has enhanced tracking accuracy than Markov Chain Monte Carlo unscented particle filter algorithm, Markov Chain Monte Carlo extended Kalman particle filter, and two-step tracking method.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.30.3-30.3
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• 2021

In order to generate the 3D structure of the 21-cm signal during the reionization, semi-numerical simulations based on Excursion set formalism are broadly used. However, semi-numerical simulations in the realization of the 3D structure are known to be the ionizing photon non-conserving by the structure of the Excursion set approach. Recently, explicit photon conserving algorithms for semi-numerical simulations introduced, but they are still too slow when forward modelling the 21-cm signal with high-dimensional parameter spaces. Here, we introduce a new method for approximately correcting photon non-conservation, which can be applied on-the-fly. This method is tailored towards the efficient simulation and Bayesian inference with high-dimensional parameter space. Then, we investigate how large an impact that photon non-conservation has on astrophysical parameter inference by performing an MCMC analysis. We find that the ionizing escape parameter is deviated from the fiducial value by 2 sigma when we infer astrophysical parameters without this correction.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1163-1170
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• 2009

Reliability analysis is of great importance in the advanced product design, which is to evaluate reliability due to the associated uncertainties. There are three types of uncertainties: the first is the aleatory uncertainty which is related with inherent physical randomness that is completely described by a suitable probability model. The second is the epistemic uncertainty, which results from the lack of knowledge due to the insufficient data. These two uncertainties are encountered in the input variables such as dimensional tolerances, material properties and loading conditions. The third is the metamodel uncertainty which arises from the approximation of the response function. In this study, an integrated method for the reliability analysis is proposed that can address all these uncertainties in a single Bayesian framework. Markov Chain Monte Carlo (MCMC) method is employed to facilitate the simulation of the posterior distribution. Mathematical and engineering examples are used to demonstrate the proposed method.

• International Journal of Reliability and Applications
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• v.13 no.1
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• pp.37-47
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• 2012

In this article we attempted reliability analysis of a component under the stress-strength pattern with both classical as well as Bayesian techniques. The main focus is made to develop the theory for dealing the reliability problems in various circumstances for bivariate environmental set up in context of Bayesian paradigm. A stress-strength based model describes the life of a component which has strength (Y) and is subjected to stress(X). We develop the Bayes and moment estimators of reliability of a component for each of the three possible conditions, under the assumption that the two stresses (i.e. $X_1$ and $X_2$) on a component are dependent and follow a Bivariate exponential (BVE) of Marshall-Olkin distribution, the strength of a component (Y) following exponential distribution is independent of the stresses. The simulation study is performed with Markov Chain Monte Carlo technique via Gibbs sampler to obtain the estimates of Bayes estimators of reliability, are compared with moment estimators of reliabilities on the basis of absolute biases.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.315-325
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• 2016

Generally, a natural river discharge is highly regulated by the hydraulic structures, and the regulated flow is substantially different from natural inflow characteristics for the use of water resources planning. The natural inflow data are necessarily required for hydrologic analysis and water resources planning. This study aimed to develop an integrated model for more reliable simulation of daily dam inflow. First, a piecewise Kernel-Pareto distribution was used for rainfall simulation model, which can more effectively reproduce the low order moments (e.g. mean and median) as well as the extremes. Second, a Bayesian Markov Chain Monte Carlo scheme was applied for the SAC-SMA rainfall-runoff model that is able to quantitatively assess uncertainties associated with model parameters. It was confirmed that the proposed modeling scheme is capable of reproducing the underlying statistical properties of discharge, and can be further used to provide a set of plausible scenarios for water budget analysis in water resources planning.

• Journal of Korea Water Resources Association
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• v.50 no.4
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• pp.277-288
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• 2017

A probabilistic risk analysis of levee system estimates the overall level of flood risk associated with the levee system, according to a series of possible flood scenarios. It requires the uncertainty analysis of all the risk components, including hydrological, hydraulic and geotechnical parts computed by employing MCMC (Markov Chain Monte Carlo), MCS (Monte Carlo Simulation) and FOSM (First-Order Second Moment), presents a joint probability combined each probability. The methodology was applied to a 12.5 km reach from upstream to downstream of the Gangjeong-Goryeong weir, including 6 levee reaches, in Nakdong river. Overtopping risks were estimated by computing flood stage corresponding to 100/200 year high quantile (97.5%) design flood causing levee overflow. Geotechnical risks were evaluated by considering seepage, slope stability, and rapid drawdown along the levee reach without overflow. A probability-based compound risk will contribute to rising effect of safety and economic aspects for levee design, then expect to use the index for riverside structure design in the future.