• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.287-301
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• 2018

It is common to encounter count data with excess zeros in various research fields such as the social sciences, natural sciences, medical science or engineering. Such count data have been explained mainly by zero-inflated Poisson model and extended models. Zero-inflated count data are also often correlated or clustered, in which random effects should be taken into account in the model. Frequentist approaches have been commonly used to fit such data. However, a Bayesian approach has advantages of prior information, avoidance of asymptotic approximations and practical estimation of the functions of parameters. We consider a Bayesian zero-inflated Poisson regression model with random effects for correlated zero-inflated count data. We conducted simulation studies to check the performance of the proposed model. We also applied the proposed model to smoking behavior data from the Regional Health Survey (2015) of the Korea Centers for disease control and prevention.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.11-19
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• 2014

In this paper, a low complexity parallel sphere decoder algorithm is proposed for high-order MIMO system. It reduces the computational complexity compared to the fixed-complexity sphere decoder (FSD) algorithm by static tree-pruning and dynamic tree-pruning using scalable node operators, and offers near-maximum likelihood decoding performance. Moreover, it also offers hardware-friendly node operation algorithm through fixing the variable computational complexity caused by the sequential nature of the conventional SD algorithm. A Monte Carlo simulation shows our proposed algorithm decreases the average number of expanded nodes by 55% with only 6.3% increase of the normalized decoding time compared to a full parallelized FSD algorithm for high-order MIMO communication system with 16 QAM modulation.

• Spatial Information Research
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• v.21 no.2
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• pp.45-54
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• 2013

Analytical and simulation error models have the ability to describe (or realize) error-corrupted versions of spatial data. But the different approaches for modeling positional errors require an internal validation that ascertains whether the analytical and simulation error models predict correct positional errors in a defined set of conditions. This paper presents stochastic simulation models of a point and a line segm ent to be validated w ith analytical error models, which are an error ellipse and an error band model, respectively. The simulation error models populate positional errors by the Monte Carlo simulation, according to an assumed error distribution prescribed by given parameters of a variance-covariance matrix. In the validation process, a set of positional errors by the simulation models is compared to a theoretical description by the analytical error models. Results show that the proposed simulation models realize positional uncertainties of the same spatial data according to a defined level of positional quality.

• Journal of Korean Tunnelling and Underground Space Association
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• v.5 no.3
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• pp.241-250
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• 2003

Rock mass is very inhomogeneous in nature and data obtained by site investigations and tests are very limited. For this reason, many uncertainties are to be included in the process of constructing structures in rock mass. In the design of a tunnel, support pattern, advance rate, and excavation method, which are important design parameters, must be determined to be optimal. However, it is not easy to determine those parameters. Moreover if those parameters are determined incorrectly, unexpected risk occurs such as decrease in the stability of a tunnel or economic loss due to the excessive supports etc. In this study, how to determine an optimal support pattern and advance rate, which are the important tunnel design parameters, is introduced based on a risk analysis. It can be confirmed quantitatively that the more supported a tunnel is, the larger reliability index becomes and the more stable the tunnel becomes. Also an optimal support pattern and advance rate can be determined quantitatively by performing a risk analysis considering construction cost and the cost of loss which can be occurred due to the collapse of a tunnel.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.5
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• pp.393-401
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• 2018

Reliability prediction of an electronic arm fire device(EAFD) was studied which is applied to prevent accidental ignition in a solid rocket motor. For predicting the reliability, the main components of the EAFD were first defined(Control unit, LEEFI, TBI) and the operating principle of each component was analyzed. Performance modeling of each part is established using selected input variables through system analysis. When complex analysis is required, we approximated it with polynomial equation using response surface method. Monte-Carlo simulation is applied to performance modeling to estimate the design reliability.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.901-910
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• 2008

Incidence of Plasmodium vivax malaria in South Korea have been reemerged from mid-1990 and infected around 1600 patients annually recent years. The authors calculated the distribution of malaria infection and prevalence in South Korea using incidence (2001-2006) and incubation period distributions by a back-calculation formula and the least squares estimation method. The estimated infection has a normal distribution with a mean 207 and a standard deviation 30.7 days. In addition, the authors found the estimated daily average prevalence is 628.8 patients.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.531-539
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• 2009

Logistic regression is widely used as a datamining technique for the customer relationship management. The maximum likelihood estimator has highly inflated variance when multicollinearity exists among the regressors, and it is not robust against outliers. Thus we propose the robust principal components logistic regression to deal with both multicollinearity and outlier problem. A procedure is suggested for the selection of principal components, which is based on the condition index. When a condition index is larger than the cutoff value obtained from the model constructed on the basis of the conjoint analysis, the corresponding principal component is removed from the logistic model. In addition, we employ an algorithm for the robust estimation, which strives to dampen the effect of outliers by applying the appropriate weights and factors to the leverage points and vertical outliers identified by the V-mask type criterion. The Monte Carlo simulation results indicate that the proposed procedure yields higher rate of correct classification than the existing method.

• Journal of radiological science and technology
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• v.40 no.1
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• pp.49-55
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• 2017

In the case of nuclear medicine practitioners in medical institutions, a wide range of exposure dose to individual workers can be found, depending on the type of source, the amount of radioactivity, and the use of shielding devices in handling radioactive isotopes. In this regard, this study evaluated the organ dose on practitioners as well as the dose reduction effect of the L-block shielding device in handling the diagnostic radiation source through the simulation based on the Monte Carlo method. As a result, the distribution of organ dose was found to be higher as the position of the radiation source was closer to the handling position of a practitioner, and the effective dose distribution was different according to the ICRP tissue weight. Furthermore, the dose reduction effect according to the L-block thickness tended to decrease, which showed the exponential distribution, as the shielding thickness increased. The dose reduction effect according to each radiation source showed a low shielding effect in proportion to the emitted gamma ray energy level.

• 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.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.279-288
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• 2000

The impact of land slope to the degree of flow divergence was considered employing distributional applications of slope exponents in the now directlOn algoriUnns. Lmear, exponential and ]X)wer law of distributional functIons were employed to address the variation of slope exponents m a terrain analysis. Dongok subwatershed at Wichun test watershed was selected as a study area. Digital Elevation Models of 20m, 30m, 40m and 50m grid size were made to perfonn the analysis. Various calcualtion methodologies of topographic index and the impact of grid sizes were investigated in terms of statistical and spatial aspects. DIstributional applications of slope e.xponents made it possible to represent the flow divergence and convergence about the ten-ain characteristics. The Monte~Carlo method was used to simulate six runoff events to check the impact of topographic factor in the runoff simulation.