• Journal of the Korean Data and Information Science Society
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• v.6 no.2
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• pp.29-39
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• 1995

A model used in the past may be altered at will in modeling for the future. For this situation, the multiprocess dynamic model provides a general framework. In this paper we consider the multiprocess discount normal model with parameters having a time dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt changes of pattern.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.193-205
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• 1997

The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers and is subject to abrupt changes in pattern. In this paper we consider the multiprocess discount generalized model with parameters having a dependent non-linear structure. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt change of pattern in parameters.

• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.277-288
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• 1997

For the analysis of survival data including covariates whose effects vary in time, the multiprocess discount survival model is proposed. The parameter vector modeling the time-varying effects of covariates is to vary between time intervals and its evolution between time intervals depends on the perturbation of the next time interval. The recursive estimation of the parameter vector can be obtained at the end of each time interval. The retrospective estimation of the survival function and the forecasting of the survival function of individuals of the specific covariates also can be obtained based on the information gathered until the end of the time interval.

• Journal of Korea Port Economic Association
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• v.37 no.2
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• pp.33-53
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• 2021

This study estimated the economic value of port redevelopment projects. The port redevelopment project consists of a combination of goods between market goods and non-market goods. The value of market goods can be measured at prices in the real market, but it is difficult to convert value estimates for non-market goods into currency values. Therefore, in this study, economic benefits of port redevelopment projects were estimated by the using the CVM. The estimated model used the Hanemann's model and the Bayesian approach to estimate the WTP of the sample group's using the single boundary model. Estimating the household's WTP, the Hanemann's model was estimated at KRW 10,038.33 and the Bayesian approach at KRW 12,217.1. As of the five-year period(discount benefits), the economic benefits of the port redevelopment project were estimated at 920.7 billion won for the Hanemann's model and 1.12 trillion won for the Bayesian model on a national basis. Meanwhile, as a result of estimating economic benefits(discount benefits) based on the administrative districts of Busan·Gyeongnam·Ulsan regions(five-year period), the Hanemann's model was estimated at KRW 140.4 billion and the Bayesian approach was estimated at KRW 170.8 billion.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.