• The Journal of Fisheries Business Administration
• /
• v.47 no.1
• /
• pp.21-29
• /
• 2016

The purpose of this study is to estimate the economic value of skin scuba marine tourism activity in Jeju Island. The economic value is estimated as consumer surplus using count data models including the truncated Poisson model and the truncated negative binominal distribution model. This study collects the effective 369 questionnaires from skin scuba marine tourists through three times in Jeju Island. The truncated Poisson model was statistically more suitable and valid than other models. The truncated Poisson model was applied to estimate consumer surplus as economic value from skin scuba in Jeju Island. A consumer surplus value per trip was estimated as about 4,081,633 won. The annual economic value from skin scuba marine tourism activity was estimated as 8,428,571 won in Jeju Island. Consequently, skin scuba marine tourism activity has a very large economic value in Jeju Island.

• Communications for Statistical Applications and Methods
• /
• v.26 no.3
• /
• pp.295-304
• /
• 2019

This article deals with threshold-asymmetric volatility models for over-dispersed and zero-inflated time series of count data. We introduce various threshold integer-valued autoregressive conditional heteroscedasticity (ARCH) models as incorporating over-dispersion and zero-inflation via conditional Poisson and negative binomial distributions. EM-algorithm is used to estimate parameters. The cholera data from Kolkata in India from 2006 to 2011 is analyzed as a real application. In order to construct the threshold-variable, both local constant mean which is time-varying and grand mean are adopted. It is noted via a data application that threshold model as an asymmetric version is useful in modelling count time series volatility.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.2
• /
• pp.177-186
• /
• 2003

The Zero-Inflated Poisson regression is a model for count data with exess zeros. When the correlated response variables are intrested, we have to extend the univariate zero-inflated regression model to multivariate model. In this paper, we study and simulate the multivariate zero-inflated regression model. A real example was applied to this model. Regression parameters are estimated by using MLE's. We also compare the fitness of multivariate zero-inflated Poisson regression model with the decision tree model.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.2
• /
• pp.395-400
• /
• 1997

An important quality characteristic of a software reliability.Software reliablilty growh models prvied the tools to evluate and moniter the reliabolty growth behavior of the sofwate during the testing phase Therefore failure data collected during the testing phase should be continmuosly analyzed on the basis of some selected software reliability growth models.For the cases where nonhomogeneous Poisson proxess models are the candiate models,we suggest Poisson regression model, which expresses the relationship between the expeted and actual failures counts in disjonint time intervals,for analyzing the failure count data.The weighted lest squares method is then used to-estimate the paramethers in the parameters in the model:The resulting estimators are equivalent to the maximum likelihood estimators. The method is illustrated by analyzing the failutr count data gathered from a large- scale switchong system.

• Safety and Health at Work
• /
• v.11 no.1
• /
• pp.61-70
• /
• 2020

Background: This study aims to assess whether the TSI PortaCount (Model 8020) is a measuring instrument comparable with the flame photometer. This would provide an indication for the suitability of the PortaCount for determining the workplace protection factor for particulate filtering facepiece respirators. Methods: The PortaCount (with and without the N95-Companion^TM) was compared with a stationary flame photometer from Moores (Wallisdown) Ltd (Type 1100), which is a measuring instrument used in the procedure for determining the total inward leakage of the particulate filtering facepiece respirator in the European Standard. Penetration levels of sodium chloride aerosol through sample respirators of two brands (A and B) were determined by the two measuring systems under laboratory conditions. For each brand, thirty-six measurements were conducted. The samples were split into groups according to their protection level, conditioning before testing, and aerosol concentration. The relationship between the gauged data from two measuring systems was determined. In addition, the particle size distribution inside the respirator and outside the respirator was documented. Linear regression analysis was used to calculate the association between the PortaCount (with and without the N95-Companion^TM) and the flame photometer. Results: A linear relationship was found between the raw data scaled with the PortaCount (without N95-Companion^TM) and the data detected by the flame photometer (R² = 0.9704) under all test conditions. The distribution of particle size was found to be the same inside and outside the respirator in almost all cases. Conclusion: Based on the obtained data, the PortaCount may be applicable for the determination of workplace protection factor.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.1
• /
• pp.45-53
• /
• 2003

The Zero-Inflated Poisson regression is a model for count data with exess zeros. When the reponse variables have excess zeros, it is not easy to apply the Poisson regression model. In this paper, we study and simulate the zero-inflated Poisson regression model. An real example was applied to this model. Regression parameters are estimated by using MLE's. We also compare the fitness of zero-inflated Poisson model with the Poisson regression and decision tree model.

• International Journal of Highway Engineering
• /
• v.17 no.6
• /
• pp.65-73
• /
• 2015

PURPOSES : This study tries to develop the accident models of 4-legged signalized intersections in Busan Metropolitan city with random parameter in count model to understanding the factors mainly influencing on accident frequencies. METHODS : To develop the traffic accidents modeling, this study uses RP(random parameter) negative binomial model which enables to take account of heterogeneity in data. By using RP model, each intersection's specific geometry characteristics were considered. RESULTS : By comparing the both FP(fixed parameter) and RP modeling, it was confirmed the RP model has a little higher explanation power than the FP model. Out of 17 statistically significant variables, 4 variables including traffic volumes on minor roads, pedestrian crossing on major roads, and distance of pedestrian crossing on major/minor roads are derived as having random parameters. In addition, the marginal effect and elasticity of variables are analyzed to understand the variables'impact on the likelihood of accident occurrences. CONCLUSIONS : This study shows that the uses of RP is better fitted to the accident data since each observations'specific characteristics could be considered. Thus, the methods which could consider the heterogeneity of data is recommended to analyze the relationship between accidents and affecting factors(for example, traffic safety facilities or geometrics in signalized 4-legged intersections).

• Journal of Technology Innovation
• /
• v.14 no.3
• /
• pp.23-42
• /
• 2006

This study investigates the determinants of output of a manufacturing firm's innovative activity (the number of patent applications) and spillover effects using a count data model. This paper attempted a negative binomial distribution In order to take into account unobserved heterogeneity. The results of our study suggested that Firm size, R&D intensity, technical network activity, and online business performance have significantly positive effects in the Korean manufacturing industry. Moreover, there are significantly positive spillover effects in the same industry sector.

• The Korean Journal of Community Living Science
• /
• v.22 no.2
• /
• pp.247-255
• /
• 2011

This study analyzed the demand for a community-based tourism site using a poisson model, a negative binominal model, a truncated poisson model and a truncated negative binominal model as count data models. For these reasons, questionnaire surveys were conducted into 5 community-based tourism sites in Chuncheon city with 406 tourists, and was analyzed using the STATA program. The fitness levels of four models were significant(p=0.0000) using a likelihood ratio test. The study results suggest that the demand of community-based tourism sites for visiting tourists was influenced by a pre-visiting experience, recognition of sustainable tourism, visitation of downtown, purchase of souvenir or farm produce, conversation with regional residents, regional harmony, preservation of natural resources and sex within the poisson and truncated poisson models. However, the variables of visitation of downtown, preservation of natural resources and sex were not significant within the negative binominal model and the visitation of downtown and preservation of natural resources were not significant within the truncated negative binominal model. The results of the visiting demand of community-based tourism sites can provide information for sustainable regional development strategies.

• Asian-Australasian Journal of Animal Sciences
• /
• v.14 no.7
• /
• pp.910-914
• /
• 2001

The teat number of a sow plays an important role for weaning pigs and has been utilized in selection of swine breeding stock. Various linear models have been employed for genetic analyses of teat number although the teat number can be considered as a count trait. Theoretically, Poisson error mixed models are more appropriate for count traits than Normal error mixed models. In this study, the two models were compared by analyzing data simulated with Poisson error. Considering the mean square errors and correlation coefficients between observed and fitted values, the Poisson generalized linear mixed model (PGLMM) fit the data better than the Normal error mixed model. Also these two models were applied to analyzing teat numbers in four breeds of swine (Landrace, Yorkshire, crossbred of Landrace and Yorkshire, crossbred of Landrace, Yorkshire, and Chinese indigenous Min pig) collected in China. However, when analyzed with the field data, the Normal error mixed model, on the contrary, fit better for all the breeds than the PGLMM. The results from both simulated and field data indicate that teat numbers of swine might not have variance equal to mean and thus not have a Poisson distribution.