• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.631-641
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• 2011

We consider a history-dependent Parrondo game in which the winning probability of the present trial depends on the results of the last two trials in the past. When a fraction of an infinite number of players are allowed to choose between two fair Parrondo games at each turn, we compare the blind strategy such as a random sequence of choices with the short-range optimization strategy. In this paper, we show that the random sequence of choices yields a steady increase of average profit. However, if we choose the game that gives the higher expected profit at each turn, surprisingly we are not supposed to get a long-run positive profit for some parameter values.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1133-1146
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• 2016

Counts or averages over arbitrary regions are often analyzed using conditionally autoregressive (CAR) models. The spatial neighborhoods within CAR model are generally formed using only the inter-distance or boundaries between the sub-regions. Kyung and Ghosh (2009) proposed a new class of models to accommodate spatial variations that may depend on directions, using different weights given to neighbors in different directions. The proposed model, directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Bayesian inference method is discussed based on efficient Markov chain Monte Carlo (MCMC) sampling of the posterior distributions of the parameters. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.92-96
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• 2000

In this paper, we propose a call admission control(CAC) scheme fer the mixed voice/data DS-CDMA systems and analyze the Er-lang capacity under the proposed CAC scheme. Voice and data traffics require different system resources based oil their Quality of Service(QoS) requirements. In the proposed CAC scheme, some system resources are reserved exclusively for handoff calls to have high priority Over new calls. Additionally the queueing of both new and handoff data traffics that are not sensitive to delay is allowed. Ar a performance measure for the suggested CAC scheme. Erlang capacity is utilized. For the performance analysis, a four-dimensional Markov chain model is developed. Erlang capacity of a practical IS-95B type system depicts, and optimum values of system parameters such as the number of reservation channels and queue lengths are found with respect to Erlang capacity. Finally, it is observed that Erlang capacity is improved more than two times by properly selecting the system parameters with the proposed CAC scheme.

• IE interfaces
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• v.23 no.4
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• pp.357-364
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• 2010

In this study, we consider performance analysis of distance-based registration (DBR). DBR causes a mobile station (MS) to register its location when the distance between the current base station (BS) and the BS in which it last registered exceeds a distance threshold D. In general, DBR has some advantages over the other registration schemes but has a tendency to causes an MS to register more frequently than zone-based registration (ZBR) that is adopted in most of mobile communication systems. The DBR with implicit registration (DBIR) was proposed to improve the performance of DBR. In this study, we point out some problems of the previous analytical model based on continuous time Markov chain and analyze exact performance of the DBIR. We show that the DBIR always outperforms the DBR by using our exact analytical model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6B
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• pp.895-900
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• 2010

As the diverse telecommunication services have been developed, network designers need to prevent congestion which may be caused by properties of timecorrelation and burstiness, and unpredictable statistical fluctuation of traffic streams. This paper considers the leaky bucket scheme with combined control of arrival and token rates, in which the arrival rate and the token generation interval are controlled according to the queue length. By using the embedded Markov chain and the supplementary variable methods, we obtain the queue length distribution as well as the loss probability and the mean waiting time.

• 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 Electrical Engineering and Technology
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• v.7 no.6
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• pp.814-823
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• 2012

Electric power transmission utilities make an effort to maximize profit by reducing their electricity supply and operation costs while maintaining their reliability. The development of maintenance strategies for aged components is one of the more effective ways to achieve this goal. The reliability centered approach is a key method in providing optimal maintenance strategies. It considers the tradeoffs between the upfront maintenance costs and the potential costs incurred by reliability losses. This paper discusses the application of the Particle Swarm Optimization (PSO) technique used to find the optimal maintenance strategy for a transmission component in order to achieve the minimum total expected cost composed of Generation Cost (GC), Maintenance Cost (MC), Repair Cost (RC) and Outage Cost (OC). Three components of a transmission system are considered: overhead lines, underground cables and insulators are considered. In regards to aged and aging component, a component state model that uses a modified Markov chain is proposed. A simulation has been performed on an IEEE 9-bus system. The results from this simulation are quite encouraging, and then the proposed approach will be useful in practical maintenance scheduling.

• IE interfaces
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• v.20 no.2
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• pp.216-226
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• 2007

In code-division multiple-access (CDMA) systems with soft handoff, mobile station (MS) within soft handoff region can use multiple radio channels and receive their signals from multiple base stations (BSs) simultaneously. In this paper, the effects of soft handoff region ratio (SHRR) on reverse link of a CDMA cellular system are analytically investigated. In order to analyze the network performance and quality of service (QoS) perceived by users more realistically, both the soft capacity increasing factor and the traffic load variation affected by SHRR are jointly considered and a two-dimensional continuous time Markov chain (CTMC) model is built. In the numerical example, it is observed that the optimal guard channel exists according the variations of the traffic load and propagation conditions when the proper value of SHRR is determined.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.627-641
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• 2016

In this paper, we consider a seemingly unrelated regression (SUR) model and propose a nonparametric Bayesian approach to SUR with a Dirichlet process mixture of normals for modeling an unknown error distribution. Posterior distributions are derived based on the proposed model, and the posterior inference is performed via Markov chain Monte Carlo methods based on the collapsed Gibbs sampler of a Dirichlet process mixture model. We present a simulation study to assess the performance of the model. We also apply the model to precipitation data over South Korea.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.43-60
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• 2018

This paper considers the use of classical and Bayesian inference methods to analyze data generated by variables whose natural behavior can be modeled using asymmetric distributions in the presence of left censoring. Our approach used a $L{\grave{e}}vy$ distribution in the presence of left censored data and covariates. This distribution could be a good alternative to model data with asymmetric behavior in many applications as lifetime data for instance, especially in engineering applications and health research, when some observations are large in comparison to other ones and standard distributions commonly used to model asymmetry data like the exponential, Weibull or log-logistic are not appropriate to be fitted by the data. Inferences for the parameters of the proposed model under a classical inference approach are obtained using a maximum likelihood estimators (MLEs) approach and usual asymptotical normality for MLEs based on the Fisher information measure. Under a Bayesian approach, the posterior summaries of interest are obtained using standard Markov chain Monte Carlo simulation methods and available software like SAS. A numerical illustration is presented considering data of thyroglobulin levels present in a group of individuals with differentiated cancer of thyroid.