• The Korean Journal of Applied Statistics
• /
• v.24 no.4
• /
• pp.677-693
• /
• 2011

In this paper, we consider Bayesian approaches to zero inflated Poisson model, one of the popular models to analyze zero inflated count data. To generate posterior samples, we deal with a Markov Chain Monte Carlo method using a Gibbs sampler and an exact sampling method using an Inverse Bayes Formula(IBF). Posterior sampling algorithms using two methods are compared, and a convergence checking for a Gibbs sampler is discussed, in particular using posterior samples from IBF sampling. Based on these sampling methods, a real data analysis is performed for Trajan data (Marin et al., 1993) and our results are compared with existing Trajan data analysis. We also discuss model selection issues for Trajan data between the Poisson model and zero inflated Poisson model using various criteria. In addition, we complement the previous work by Rodrigues (2003) via further data analysis using a hierarchical Bayesian model.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.1
• /
• pp.190-197
• /
• 2010

The ACR (Access Control Router) of WiBro processes location registration of the Correspondent Node and Home Agent as the Correspondent Node moves between ACRs. Therefore, the location update cost is low compared with MIPv6. However, all packets which are sent and received are sent through the ACR, so as the number of mobile nodes that are managed by the ACR increases, the cost of packet delivery also increases. Therefore, the communication state of the ACR domain remains smooth when the ACR which manages the mobile node in the ACR domain has good performance. However, network delays occur unless the ACR performs well, so the role of the ACR is important. In this paper, we analysis performance of the ACR for efficient realization of the WiBro standard. By using the Deny Probability and the Total Profit of ACR performance and apply it to the Random Walk Mobility model as the mobility model.

• Environmental and Resource Economics Review
• /
• v.19 no.2
• /
• pp.215-242
• /
• 2010

This study constructs a bayesian neoclassical DSGE model that applies oil usage. The model includes technology shocks, oil price shocks, and shocks to energy policies as exogenous driving forces. First, this study aims to analyze the roles of these exogenous shocks in the Korean business cycle. Second, this study examines the effects of long-term changes in the energy consumption structure, including the reduction in oil use as a share of energy consumption and improvement in oil efficiency. In the case of oil price shocks, results show that these shocks exert recessionary pressure on the economy in line with those obtained in the previous literature. On the other hand, shocks to energy policies, which reduce oil consumption per capital, result in opposite consequences to oil price shocks, decreasing oil consumption. Also, counterfactual exercises show that long-term changes in the energy consumption structure would mitigate the contractionary effects of oil price shocks.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.6B
• /
• pp.697-709
• /
• 2011

For improvement of wireless Internet service quality at vehicle's moving speed, it is advised to reduce the service disruption time by reducing the handover frequency on vehicle's moving path. Particularly, it is advantageous to avoid the handover to cell whose dwell time is short or can be ignored in terms of service continuity and average throughput. This paper proposes the handover scheme that is suitable for vehicle in order to improve the wireless Internet service quality. In the proposed scheme, the handover process continues to be learned before being modeled to Discrete-Time Markov Chain (DTMC). This modeling reduces the handover frequency by preventing the handover to cell that could provide service sufficiently to passenger even when vehicle passed through the cell but there was no need to perform handover. In order to verify the proposed scheme, we observed the average number of handovers, the average RSSI and the average throughput on various moving paths that vehicle moved in the given urban environment. The experiment results confirmed that the proposed scheme was able to provide the improved wireless Internet service to vehicle that moved to some degree of consistency.

• Journal of Korean Society on Water Environment
• /
• v.27 no.5
• /
• pp.634-645
• /
• 2011

A newly developed rainwater harvesting (RWH) system reliability model is evaluated for roof area of buildings in Haeundae District of Busan. RWH system is used to supply water for toilet flushing, back garden irrigation, and air cooling. This model is portable because it is based on a non-parametric precipitation generation algorithm using a markov chain. Precipitation occurrence is simulated using transition probabilities derived for each day of the year based on the historical probability of wet and dry day state changes. Precipitation amounts are selected from a matrix of historical values within a moving 30 day window that is centered on the target day. Then, the reliability of RWH system is determined for catchment area and tank volume ranges using synthetic precipitation data. As a result, the synthetic rainfall data well reproduced the characteristics of precipitation in Busan. Also the reliabilities of RWH system for each of demands were computed to high values. Furthermore, for study area using the RWH system, reduction efficiencies for rooftop runoff inputs to the sewer system and potable water demand are evaluated for 23%, 53%, respectively.

• Journal of Korean Society of Forest Science
• /
• v.89 no.5
• /
• pp.655-665
• /
• 2000

Taking account of the structural variation on species composition by topography, the successional trends were comparatively analyzed for the three topographic positions (valley, mid-slope, and ridge) in the natural deciduous forest of Mt. Chumbong area. The analysis was based upon the subsequent process of generation replacement by understory saplings and seedlings over the overstory trees which will be eventually fallen down. This study adopted the plot sampling method, establishing twenty $20m{\times}20m$ quadrats and collecting vegetation and site data on each different topographic position. The transition matrix model, which was modified from the mathematical theory of Markov chain, was employed to analyze the successional trends and thereafter to predict the overstory species composition in the future for each different topographic position. In valley, the simulation indicated the remarkable decrease in the proportion of species composition of present dominants Quercus mongolica and Fraxinus mandshurica from current 23% and 21% to around 4% of each at the steady state, which is predicted to take less than 200 years. On the other hand, the proportion of such species as Abies holophylla, Acer mono, Tilia amurensis, and Ulmus laciniata will increase at the steady state. In mid-slope, the result showed the remarkable decrease in the proportion of Juglans mandshurica, Kalopanax pictus, and Tilia amurensis from current 15%, 8%, and 15% to 2%, 1%, and 5%, respectively, at steady state predicted to take more than 250 years. In ridge, the current dominant Quercus mongolica was predicted to be decreased dramatically from 58% to 8% at steady state which could be achieved about 200 years. On the contrary, the proportion of Acer mono and Tilia amurensis will be increased from current 4% and 3% to more than 20% and 40%, respectively, at the steady state. Overall results suggested that the study forest is more likely seral rather than climax community. Even though a lot of variation is inevitable due to various kinds of site and vegetation development, the study forest is considered to be more than 200 years away from the steady state or climax in terms of overstory species composition.

• Journal of Life Science
• /
• v.21 no.3
• /
• pp.385-392
• /
• 2011

The otter (Lutra lutra) in Korea is classified as a first grade endangered species and is managed under state control. We performed a phylogenetic analysis of the otter that inhabits the Changnyeong, Jinju, and Geoje areas in Gyeongsangnamdo, Korea using mtDNA and microsatellite (MS) markers. As a result of the analysis using the 676-bp D-loop sequence of mtDNA, six haplotypes were estimated from five single nucleotide polymorphisms. The genetic distance between the Jinju and Geoje areas was greater than distances within the areas, and the distance between Jinju and Geoje was especially clear. From the phylogenetic tree estimated using the Bayesian Markov chain Monte Carlo analysis by the MrBays program, two subgroups, one containing samples from Jinju and the other containing samples from the Changnyeong and Geoje areas were clearly identified. The result of a parsimonious median-joining network analysis also showed two clear subgroups, supporting the result of the phylogenetic analysis. On the other hand, in the consensus tree estimated using the genetic distances estimated from the genotypes of 13 MS markers, there were clear two subgroups, one containing samples from the Jinju, Geoje and Changnyeong areas and the other containing samples from only the Jinju area. The samples were not identically classified into each subgroup defined by mtDNA and MS markers. It could be inferred that the differential classification of samples by the two different marker systems was because of the different characteristics of the marker systems used, that is, the mtDNA was for detecting maternal lineage and the MS markers were for estimating autosomal genetic distances. Nonetheless, the results from the two marker systems showed that there has been a progressive genetic fixation according to the habitats of the otters. Further analyses using not only newly developed MS markers that will possess more analytical power but also the whole mtDNA are needed. Expansion of the phylogenetic analysis using otter samples collected from the major habitats in Korea should be helpful in scientifically and efficiently maintaining and preserving them.

• The Korean Journal of Applied Statistics
• /
• v.31 no.2
• /
• pp.287-301
• /
• 2018

It is common to encounter count data with excess zeros in various research fields such as the social sciences, natural sciences, medical science or engineering. Such count data have been explained mainly by zero-inflated Poisson model and extended models. Zero-inflated count data are also often correlated or clustered, in which random effects should be taken into account in the model. Frequentist approaches have been commonly used to fit such data. However, a Bayesian approach has advantages of prior information, avoidance of asymptotic approximations and practical estimation of the functions of parameters. We consider a Bayesian zero-inflated Poisson regression model with random effects for correlated zero-inflated count data. We conducted simulation studies to check the performance of the proposed model. We also applied the proposed model to smoking behavior data from the Regional Health Survey (2015) of the Korea Centers for disease control and prevention.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.6
• /
• pp.789-792
• /
• 2004

In various fields as web mining, bioinformatics, statistical data analysis, and so forth, very diversely missing values are found. These values make training data to be sparse. Largely, the missing values are replaced by predicted values using mean and mode. We can used the advanced missing value imputation methods as conditional mean, tree method, and Markov Chain Monte Carlo algorithm. But general imputation models have the property that their predictive accuracy is decreased according to increase the ratio of missing in training data. Moreover the number of available imputations is limited by increasing missing ratio. To settle this problem, we proposed statistical learning theory to preprocess for missing values. Our statistical learning theory is the support vector regression by Vapnik. The proposed method can be applied to sparsely training data. We verified the performance of our model using the data sets from UCI machine learning repository.

• Journal of the Korea Society for Simulation
• /
• v.23 no.3
• /
• pp.19-25
• /
• 2014

Demand forecasting is the basis of management activities including marketing strategy. Especially, the demand of a part is remarkably important in supply chain management (SCM). In the fields of various industries, the part demand usually has the intermittent characteristic. The intermittent characteristic implies a phenomenon that there frequently occurs zero demands. In the intermittent demands, non-zero demands have large variance and their appearances also have stochastic nature. Accordingly, in the intermittent demand forecasting, it is inappropriate to apply the traditional time series models and/or cause-effect methods such as linear regression; they cannot describe the behaviors of intermittent demand. Markov bootstrap method was developed to forecast the intermittent demand. It assumes that first-order autocorrelation and independence of lead time demands. To release the assumption of independent lead time demands, this paper proposes a modified bootstrap method. The method produces the pseudo data having the characteristics of historical data approximately. A numerical example for real data will be provided as a case study.