• ETRI Journal
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• v.29 no.4
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• pp.527-529
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• 2007

In this paper, a decision-tree-based Markov model for phrase break prediction is proposed. The model takes advantage of the non-homogeneous-features-based classification ability of decision tree and temporal break sequence modeling based on the Markov process. For this experiment, a text corpus tagged with parts-of-speech and three break strength levels is prepared and evaluated. The complex feature set, textual conditions, and prior knowledge are utilized; and chunking rules are applied to the search results. The proposed model shows an error reduction rate of about 11.6% compared to the conventional classification model.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.19-35
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• 2006

We use the hierarchical Bayesian approach to describe the transition probabilities of a binary nonhomogeneous Markov chain. The Markov chain is used for describing the transition behavior of emotionally disturbed children in a treatment program. The effects of covariates on transition probabilities are assessed using a logit link function. To describe the time evolution of transition probabilities, we consider two modeling strategies. The first strategy is based on the concept of exchangeabiligy, whereas the second one is based on a first order Markov property. The deviance information criterion (DIC) measure is used to compare models with two different time dependent structures. The inferences are made using the Markov chain Monte Carlo technique. The developed methodology is applied to some real data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.664-682
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• 2012

To improve the accuracy and enhance the applicability of existing models, this paper proposes a generalized Markov chain model for IEEE 802.11 Distributed Coordination Function (DCF) under the widely adopted assumption of ideal transmission channel. The IEEE 802.11 DCF is modeled by a two dimensional Markov chain, which takes into account unsaturated traffic, backoff freezing, retry limits, the difference between maximum retransmission count and maximum backoff exponent, and limited buffer size based on the M/G/1/K queuing model. We show that existing models can be treated as special cases of the proposed generalized model. Furthermore, simulation results validate the accuracy of the proposed model.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.29-42
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• 2018

We combine the integer-valued GARCH(1, 1) model with a generalized regime-switching model to propose a dynamic count time series model. Our model adopts Markov-chains with time-varying dependent transition probabilities to model dynamic count time series called the generalized regime-switching integer-valued GARCH(1, 1) (GRS-INGARCH(1, 1)) models. We derive a recursive formula of the conditional probability of the regime in the Markov-chain given the past information, in terms of transition probabilities of the Markov-chain and the Poisson parameters of the INGARCH(1, 1) process. In addition, we also study the forecasting of the Poisson parameter as well as the cumulative impulse response function of the model, which is a measure for the persistence of volatility. A Monte-Carlo simulation is conducted to see the performances of volatility forecasting and behaviors of cumulative impulse response coefficients as well as conditional maximum likelihood estimation; consequently, a real data application is given.

• Management Science and Financial Engineering
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• v.14 no.2
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• pp.105-118
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• 2008

There are many debates on the topic of the relationship between oil prices and economic growth. Through the repeated processes of conformations and contractions on the subject, two main issues are developed; one is how to define and drive oil shocks from oil prices, and the other is how to specify an econometric model to reflect the asymmetric relations between oil prices and output growth. The study, thus, introduces the unobserved component model to pick up the oil shocks and a first-order Markov switching model to reflect the asymmetric features. We finally employ unique oil shock variables from the stochastic trend components of oil prices and adapt four lags of the mean growth Markov Switching model. The results indicate that oil shocks exert more impact to recessionary state than expansionary state and the supply-side oil shocks are more persistent and significant than the demand-side shocks.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.99-100
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• 2014

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.4
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• pp.249-253
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• 2012

This paper suggests a numerical method for valuation of American options under the Kou model (double exponential jump diffusion model). The method is based on approximation of underlying asset price using a finite-state, time-homogeneous Markov chain. We examine the effectiveness of the proposed method with simulation results, which are compared with those from the conventional numerical method, the finite difference method for PIDE (partial integro-differential equation).

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.1-9
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• 2007

A three-state Markov logistic regression model is suggested to forecast the probability of tomorrow's precipitation based on the current meteorological situation. The suggested model turns out to be better than Markov regression model in the sense of the mean squared error of forecasting for the rainfall data of Seoul area.

• Management Science and Financial Engineering
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• v.19 no.2
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• pp.37-42
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• 2013

This paper suggests a numerical method for valuation of European and American options under the two L$\acute{e}$vy Processes, Normal Inverse Gaussian Model and the Variance Gamma model. The method is based on approximation of underlying asset price using a finite-state, time-homogeneous Markov chain. We examine the effectiveness of the proposed method with simulation results, which are compared with those from the existing numerical method, the lattice-based method.

• Water for future
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• v.14 no.4
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• pp.19-26
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• 1981

The auto-and cross-correlation function, power spectrum, coherence function and Markov model are applied to investigate the statistical characteristics of discharge and each factor of water quality and the interrelation-ship between the variation of discharge and water quality factors. The analysis of discharge, dissolved oxygen and electric conductivity, which were only obtainable data at the Indogyo gagining station in the downstream of the Han River, clearly showed that they hace distinct period of 12 months and three different periods of 6, 4 and 3 months weaker than the former. The cross-correlation between the discharge and water quality(DO, COND) is rather weak and the crosscorrelation function has its peak at lag one. It is considered therefrom that the variation of discharge behaves on water quality facotrs with one day's difference. In the examination of linear regression model for the serial generation and predictive measures, discharge series is fit to first and second order Markov model and DO, COND to first order Markov model.