• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.187-194
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• 2014

We consider a Bayesian nonignorable selection model to accommodate the selection bias. Markov chain Monte Carlo methods is known to be very useful to fit the nonignorable selection model. However, sensitivity to prior assumptions on parameters for selection mechanism is a potential problem. To quantify the sensitivity to prior assumption, the deviance information criterion and the conditional predictive ordinate are used to compare the goodness-of-fit under two different prior specifications. It turns out that the 'MLE' prior gives better fit than the 'uniform' prior in viewpoints of goodness-of-fit measures.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.245-254
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• 2014

The traditional methods of simulating daily precipitation have paid little attention to the inherent dependence structure between the total precipitation amount and the precipitation frequency for a fixed period of time. To address this issue, we propose a new simulation algorithm using copula in order to incorporate the dependence into the traditional methods. The algorithm consists of two parts: First, while reflecting the observed dependence, we generate the total precipitation amount (S) and the frequency (N) during the period of interest; then we simulate the daily precipitation whose aggregation matches the pair of (N; S) generated in the first part. Our result shows that the proposed method substantially improves the traditional methods.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.1037-1044
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• 2012

Multivariate control charts for effectively monitoring every component in the dispersion matrix of multivariate normal process are considered. Through the numerical results, we noticed that the multivariate control charts based on sample statistic $V_i$ by Hotelling or $W_i$ by Alt do not work effectively when the correlation coefficient components in dispersion matrix are increased. We propose a combined procedure monitoring every component of dispersion matrix, which operates simultaneously both control charts, a chart controlling variance components and a chart controlling correlation coefficients. Our numerical results show that the proposed combined procedure is efficient for detecting changes in both variances and correlation coefficients of dispersion matrix.

• ETRI Journal
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• v.32 no.2
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• pp.292-301
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• 2010

Increasing convergence among heterogeneous radio networks is expected to be a key feature of future ubiquitous services. The convergence of radio networks in combination with dynamic spectrum allocation (DSA) could be a beneficial means to solve the growing demand for radio spectrum. DSA might enhance the spectrum utilization of involved radio networks to comply with user requirements for high-quality multimedia services. This paper proposes a simple spectrum allocation algorithm and presents an analytical model of dynamic spectrum resource allocation between two networks using a 4-D Markov chain. We argue that there may exist a break-even point for choosing whether or not to adopt DSA in a system. We point out certain circumstances where DSA is not a viable alternative. We also discuss the performance of DSA against the degree of resource sharing using the proposed analytical model and simulations. The presented analytical model is not restricted to DSA, and can be applied to a general resource sharing study.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.583-589
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• 2004

Modem control systems often use a communication network to send measurement and control signals between nodes. Communication delays can be time varying. The length of the time delays is often hard to predict and modeled as being random. This paper proposes a combined controller used to compensate network time delay by estimating the delay with the interacting multiple model (IMM). The network delay is modeled as a Markov chain and 3 modes representing heavy, medium, and low network loads are used in the IMM. The proposed method is applied to an optimal control system with double integrators and the results are compared with the existing control methods.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.915-920
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• 2003

Modern control systems often use a communication network to send measurement and control signals between nodes. Communication delays can be time varying. The length of the time delays is often hard to predict and are modeled as being random. This paper proposes a combined controller used to compensate network time delay by estimating the delay with the interacting multiple model (IMM). The network delay is modeled as a Markov chain and 3 modes representing heavy, medium, and low network loads are used in the IMM. The proposed method is applied to an optimal control system with double integrators and the results are compared with the existing control methods.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.121-124
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• 2000

In IMT-2000 systems, the outer loop dynamically adjusts the target SIR so that adequate performance in terms of the frame error rate(FER) and the true quality measure is achieved. This paper utilizes an analytic model lot outer loop power control(OLPC) adjusting the target SIR in IMT-2000. The analytic model is based on the discrete-time Markov chain as voice traffic SIR. It is described that the model can be used to find the optimum step size in voice traffic for fast fading environments. The optimum step size influences the performance of OLPC： As the step size decreases, the average target SIR increases and average FER decreases.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.1
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• pp.30-37
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• 2016

A multi-server queueing system with an infinite buffer and impatient customers is analyzed. The system operates in the finite state Markovian random environment. The number of available servers, the parameters of the batch Markovian arrival process, the rate of customers' service, and the impatience intensity depend on the current state of the random environment and immediately change their values at the moments of jumps of the random environment. Dynamics of the system is described by the multi-dimensional asymptotically quasi-Toeplitz Markov chain. The ergodicity condition is derived. The main performance measures of the system are calculated. Numerical results are presented.

• Journal of the Korean Statistical Society
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• v.35 no.4
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• pp.419-439
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• 2006

The article develops a new class of distributions by introducing a nonnegative perturbing function to $t_\nu$ distribution having location and scale parameters. The class is obtained by using transformations and conditioning. The class strictly includes $t_\nu$ and $skew-t_\nu$ distributions. It provides yet other models useful for selection modeling and robustness analysis. Analytic forms of the densities are obtained and distributional properties are studied. These developments are followed by an easy method for estimating the distribution by using Markov chain Monte Carlo. It is shown that the method is straightforward to specify distribution ally and to implement computationally, with output readily adopted for constructing required criterion. The method is illustrated by using a simulation study.

• Journal of the Korean Statistical Society
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• v.36 no.2
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• pp.321-333
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• 2007

In the proportional hazard model with the beta process prior, the posterior computation with the discrete approximation is considered. The time period of interest is partitioned by small intervals. On each partitioning interval, the likelihood is approximated by that of a binomial experiment and the beta process prior is by a beta distribution. Consequently, the posterior is approximated by that of many independent binomial model with beta priors. The analysis of the leukemia remission data is given as an example. It is illustrated that the length of the partitioning interval affects the posterior and one needs to be careful in choosing it.