• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.239-263
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• 1998

In this paper, we consider a multistate survival model which incorporates covariates and contains two illness states and two death states. The underlying stochastic process is assumed to follow nonhomogeneous Markov process. The estimates of survival, transition and competing risks probabilities are given via the methods of partial likelihood and nonparametric maximum likelihood. Our discussion is based on the statistical theory of counting process. An illustration is given to the data of patients in a heart transplant program. The goodness of fit procedures are also discussed to check the adequacy of the model.

• The KIPS Transactions:PartD
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• v.10D no.5
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• pp.805-812
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• 2003

This paper presents a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process (NHPP) and performs Bayesian inference using prior information. The failure process is analyzed to develop a suitable mean value function for the NHPP; expressions are given for several performance measure. The parametric inferences of the model using Logarithmic Poisson model, Crow model and Rayleigh model is discussed. Bayesian computation and model selection using the sum of squared errors. The numerical results of this models are applied to real software failure data. Tools of parameter inference was used method of Gibbs sampling and Metropolis algorithm. The numerical example by T1 data (Musa) was illustrated.

• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.251-264
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• 2008

In this study, a nonhomogeneous markov model which is able to simulate hourly rainfall series is developed for estimating reliable hydrologic variables. The proposed approach is applied to simulate hourly rainfall series in Korea. The simulated rainfall is used to estimate the design rainfall and flood in the watershed, and compared to observations in terms of reproducing underlying distributions of the data to assure model's validation. The model shows that the simulated rainfall series reproduce a similar statistical attribute with observations, and expecially maximum value is gradually increased as number of simulation increase. Therefore, with the proposed approach, the non-homogeneous markov model can be used to estimate variables for the purpose of design of hydraulic structures and analyze uncertainties associated with rainfall input in the hydrologic models.

• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1269-1276
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• 2004

The Parameter Estimation for software existing reliability models, Goel-Okumoto, Yamada-Ohba-Osaki model was reviewed and Rayleigh model based on Rayleigh distribution was studied. In this paper, we discusses comparison of parameter estimation using maximum likelihood estimator and Bayesian estimation based on Gibbs sampling to analysis of the estimator' pattern. Model selection based on sum of the squared errors and Braun statistic, for the sake of efficient model, was employed. A numerical example was illustrated using real data. The current areas and models of Superposition, mixture for future development are also employed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.139-144
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• 2000

Recently, a homogeneous fuzzy time series model was proposed by means of defining some new operations on fuzzy numbers. In this paper, we consider expanding the results to the nonhomogeneous fuzzy time series and the general fuzzy time series using Tw, the weakest t-norm, based algebraic fuzzy operations.

• International Journal of Quality Innovation
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• v.5 no.1
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• pp.110-121
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• 2004

There is a new trend of incorporating software coverage metrics into software reliability modelling. This paper proposes a coverage-based software reliability growth model. Firstly, the failure rate function in coverage is analytically derived. Then it is shown that the number of detected faults follows a Nonhomogeneous Poisson distribution of which intensity function is the failure rate function in coverage. Practical applicability of the proposed models is examined by illustrative numerical examples.

• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.451-459
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• 2011

In this paper, a software release model is presented to determine the optimum testing time with consideration of software error type. The software errors are classified into two types, major and minor errors. The software testing is continued until the Nth major error is discovered and corrected. The total cost needed before and after testing time is modeled under nonhomogeneous Poisson error correction model. Numerical examples are presented to demonstrate the results.

• Journal of the military operations research society of Korea
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• v.18 no.2
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• pp.96-113
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• 1992

A time-varying nonhomogeneous poisson process approximation of the nonexponential stochastic Lanchester model is defined and evaluated over a range of combat parameters including initial force sizes. breakpoints. and interkilling random variables. The proposed approximation is far excellent and takes much less CPU time than the existing models. The sensitivity analysis was peformed to evaluate the efficiency of the proposed model and three recommended factors are suggested to guide the combat operators.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.393-400
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• 2001

This paper presents a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process (NHPP). The failure process is analyzed to develop a suitable mean value function for the NHPP ; expressions are given for several performance measure. Actual software failure data are compared with generalized model by Goel dependent on the constant reflecting the quality of testing. The performance measures and parametric inferences of the new models, Rayleigh and Gumbel distributions, are discussed. The results of the new models are applied to real software failure data and compared with Goel-Okumoto and Yamada, Ohba and Osaki models. Tools of parameter inference was used method of the maximun likelihood estimate and the bisection algorithm for the computing nonlinear root. In this paper, using the sum of the squared errors, model selection was employed. The numerical example by NTDS data was illustrated.

• Spatial Information Research
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• v.6 no.1
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• pp.77-90
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• 1998

The complex interactions of climate, topography, geology, biota and hwnan activities result in the land cover patterns, which are impacted by natural disturbances such as fire, earthquake and flood. Natural disturbances disrupt ecosystem communities and change the physical environment, thereby generating a new landscape. Community ecologists believe that disturbance is critical in determining how diverse ecological systems function. Fires were once a major agent of disturbance in the North American tall grass prairies, African savannas, and Australian bush. The major focus of this research was to develop stochastic model of spatial process of disturbance or spatial events and simulate the process based on the developed model and it was applied to the fire arrival process in the Great Victoria Desert of Australia, where wildfires generate a mosaic of patches of habitat at various stages of post-fire succession. For this research, Landsat Multi-Spectral Scanner(MSS) data covering the period from 1972 to 1994 were utilized. Fire arrival process is characterized as a spatial point pattern irregularly distributed within a region of space. Here, nonhomogeneous planar Poisson process is proposed as a model for the fire arrival process and rejection sampling thinning the homogeneous Poisson process is used for its simulation.