• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.571-579
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• 2011

We propose a new bivariate zero-inflated negative binomial regression model to allow heterogeneous dispersions. To show the performance of our proposed model, Health Care data in Deb and Trivedi (1997) are used to compare it with the other bivariate zero-inflated negative binomial model proposed by Wang (2003) that has a common dispersion between the two response variables. This empirical study shows better results from the views of log-likelihood and AIC.

• The Journal of Bigdata
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• v.3 no.2
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• pp.101-112
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• 2018

The purpose of the study is to predict the number of police calls using neural network which is one of the machine learning and negative binomial regression, by using the data of 112 police calls received from Chungnam Provincial Police Agency from June 2016 to May 2017. The variables which may affect the police calls have been selected for developing the prediction model : time, holiday, the day before holiday, season, temperature, precipitation, wind speed, jurisdictional area, population, the number of foreigners, single house rate and other house rate. Some variables show positive correlation, and others negative one. The comparison of the methods can be summarized as follows. Neural network has correlation coefficient of 0.7702 between predicted and actual values with RMSE 2.557. Negative binomial regression on the other hand shows correlation coefficient of 0.7158 with RMSE 2.831. Neural network has low interpretability, but an excellent predictability compared with the negative binomial regression. Based on the prediction model, the police agency can do the optimal manpower allocation for given values in the selected variables.

• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.178-184
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• 2012

This study deals with the traffic accidents by weather condition. The objectives are to comparatively analyze the characteristics, and to develop the models of traffic accidents by weather condition. In pursuing the above, this paper gives particular attentions to testing the differences between two groups, and developing the models(Poisson and negative binomial regression) using the data of domestic circular intersections. The main results are as follows. First, three Poisson models and one negative binomial models which were all statistically significant were developed using the number of accident and EPDO by the clear weather and other as the dependant variables. Second, the differences between two models were comparatively analyzed using the chosen variables. This paper might be expected to give some implications to traffic safety policy-making to reduce and prevent the traffic accidents in circular intersections.

• Journal of Korean Academy of Nursing
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• v.42 no.6
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• pp.819-832
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• 2012

Purpose: This study was conducted to identify risk factors that influence the probability and severity of elder abuse in community-dwelling older adults. Methods: This study was a cross-sectional descriptive study. Self-report questionnaires were used to collect data from community-dwelling Koreans, 65 and older (N=416). Logistic regression, negative binomial regression and zero-inflated negative binomial regression model for abuse count data were utilized to determine risk factors for elder abuse. Results: The rate of older adults who experienced any one category of abuse was 32.5%. By zero-inflated negative binomial regression analysis, the experience of verbal-psychological abuse was associated with marital status and family support, while the experience of physical abuse was associated with self-esteem, perceived economic stress and family support. Family support was found to be a salient risk factor of probability of abuse in both verbal-psychological and physical abuse. Self-esteem was found to be a salient risk factor of probability and severity of abuse in physical abuse alone. Conclusion: The findings suggest that tailored prevention and intervention considering both types of elder abuse and target populations might be beneficial for preventative efficiency of elder abuse.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.859-864
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• 2012

Poisson regression and negative binomial regression are usually used to analyze counting data; however, these models are unsuitable for fit zero-inflated data that contain unexpected zero-valued observations. In this paper, we review the zero-inflated regression in which Bernoulli process and the counting process are hierarchically mixed. It is known that zero-inflated regression can efficiently model the over-dispersion problem. Vuong statistic is employed to compare performances of the zero-inflated models with other standard models.

• Journal of the Ergonomics Society of Korea
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• v.25 no.4
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• pp.23-33
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• 2006

The purpose of this study is to determine the economical standard of road signs by verifying the difference of driver's recognition and reaction time according to the space rate of letters on the road signs. For this reason, indoor simulations was conducted to confirm difference of recognition and reaction time on six sign-targets having different space rate. Also, a negative binomial regression model was used to find the main factors which could lower the rate of misreading. For this model, increasing of legibility of sign is not only simple enlargement of sign, but also suitable match of letters and sign. The result of this study is capable of verifying the importance of the space rate in road signs, and being utilized as a effective method to determine the standard of the road signs.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.311-325
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• 2022

For count responses, the situation of excess zeros often occurs in various research fields. Zero-inflated model is a common choice for modeling such count data. Bayesian inference for the zero-inflated model has long been recognized as a hard problem because the form of conditional posterior distribution is not in closed form. Recently, however, Pillow and Scott (2012) and Polson et al. (2013) proposed a Pólya-Gamma data-augmentation strategy for logistic and negative binomial models, facilitating Bayesian inference for the zero-inflated model. We apply Bayesian zero-inflated negative binomial regression model to longitudinal pharmaceutical data which have been previously analyzed by Min and Agresti (2005). To facilitate posterior sampling for longitudinal zero-inflated model, we use the Pólya-Gamma data-augmentation strategy.

• The Journal of Fisheries Business Administration
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• v.36 no.1 s.67
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• pp.81-101
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• 2005

This article is to estimate the fishing frequency function in Korean recreational fishery with respect to socio-economic characteristics of anglers. First, the study described the characteristics of the entire angler population on the view points of 9 socio-economic variables. And then, the study divided the total angler population into three groups of in-land, sea, and mixed angler populations in order to investigate the differences in their characteristics. The study could confirm the existence of differences in regions, size of regions, and educational levels between the in - land and the sea angler populations by testing heterogeneity in the frequency table. The fishing frequency function is estimated using Poisson regression model in order to accomodate the count data(non-negative discrete random variable) aspects of the fishing frequency. However, the model specification error is found due to overdispersion of data. The model exhibits the lack of goodness of fit. The negative binomial regression model is adopted to cure the overdispersion of the data as an alternative estimation methodology. Finally, the study can confirm overdispersion does not exist in the model any more and the goodness of fit improved significantly to the reasonable level. The results of estimation of fishing frequency population modeled by the negative binomial regression models are following. The three variables of region, sex, and education have effects on the decision making process of fishing frequency in the case of in-land recreation fishery. On the other hand, the three variables of sex, age, and marriage status do the same job in the case of sea angler population. Among the left-over variables, both income and use of Internet variables now affect on the process in mixed angler population. Finally, the results of whole angler population show that all of the previous variables are proven to be statistically significant due to the summation of data with all three sub-groups of angler population.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.387-397
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• 2015

In this paper, the determinants of the number of job changes in the SMEs (small and medium enterprises) youth-intern project is analysed, utilizing SMEs youth-intern DB and employment insurance DB. Since the number of job changes are count data which take integer values other than negative values, general linear regression analysis becomes inappropriate. Therefore, four models such as Poisson regression model, zero inflated Poisson regression model, negative binomial regression model and zero inflated negative binomial regression model are tried to fit count data. A zero inflated negative binomial regression model is selected to be the best model. Major results are the followings. First, the number of job changes is shown to be significantly smaller in the treatment group than in the control group. Second, the number of job changes turns out to be significantly smaller in the young-age group than in the old-age group. Third, it is also shown that the number of job changes of man is significantly greater than that of woman. Lastly, the number of job changes in the bigger firm is shown to be significantly less than that of the smaller firm.

• Journal of Navigation and Port Research
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• v.48 no.1
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• pp.62-77
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• 2024

In the maritime shipping industry, imbalance between supply and demand has persistently increased, leading to the utilization of blank sailings by major shipping companies worldwide as a key means of flexibly adjusting vessel capacity in response to shipping market conditions. Traditionally, blank sailings have been frequently implemented around the Chinese New Year period. However, due to unique circumstances such as the global pandemic starting in 2020 and trade tensions between the United States and China, shipping companies have recently conducted larger-scale blank sailings compared to the past. As blank sailings directly impact freight transport delays, they can have negative repercussions from perspectives of both businesses and consumers. Therefore, this study employed Poisson regression models and negative binomial regression models to analyze the influence of maritime freight rate determinants on shipping companies' decisions regarding blank sailings, aiming to proactively address potential consequences. Results of the analysis indicated that, in Poisson regression analysis for 2M, significant variables included global container shipping volume, container vessel capacity, container ship scrapping volume, container ship newbuilding index, and OECD inflation. In negative binomial regression analysis, ocean alliance showed significance with global container shipping volume and container ship order volume, the alliance with container ship capacity and interest rates, non-alliance with international oil prices, global supply chain pressure index, container ship capacity, OECD inflation, and total alliance with container ship capacity and interest rates.