• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1117-1125
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• 2012

The occurrence of food poisoning is usually modeled by meteorological variables like the temperature and the humidity. In this paper, we investigate the relationship between food poisoning occurrence and climate variables in Korea and compare Poisson regression and autoregressive moving average model to select the forecast model. We confirm that lagged climate variables affect the food poisoning occurrences. However, it turns out that, from the viewpoint of the prediction, the number of previous occurrences is more influential to the current occurrence than meteorological variables and Poisson regression model is less reliable.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1337-1347
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• 2016

This study investigates factors affecting the number of insurance solicitor's turnovers of life insurance companies based on questionnaire about them. Since the response variable which is the number of insurance solicitor's turnovers is count data, it is analyzed by Poisson regression which is one of generalized regression. When work year in current company, which is direct influential factor on the number of insurance solicitor's turnovers, is controlled, affiliated corporation has been found to be the most influential factor. In addition, age, motivation to work as financial planner, monthly income, a number of average new contract per month, and final education have been identified to be important factors. If insurance solicitor's occupant organization is large company, the number of turnovers becomes small, but if the organization is general agent(GA), it becomes larger. When insurance solicitor's age is high, the number of insurance solicitor's turnovers are reduced. If the motivation to become a financial planner is due to acquaintance such as family and relatives, the number of turnovers becomes small.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.603-613
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• 2008

We often encounter the situation that discrete count data have a large portion of zeros. In this case, it is not appropriate to analyze the data based on standard regression models such as the poisson or negative binomial regression models. In this article, we consider Bayesian analysis for two commonly used models. They are zero-inflated poisson and negative binomial regression models. We use the Bayes factor as a model selection tool and computation is proceeded via Markov chain Monte Carlo methods. Crash count data are analyzed to support theoretical results.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.819-827
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• 2009

Kim et al. (2006) analyzed the donation data surveyed by Voluneteer 21 in year 2002 at South Korea using a Poisson regression based on the mixture of two Poissons and detected significant variables for affecting the number of donations. However, noting the large deviation between the predicted and the actual frequencies of zero, we developed in this note a Poisson regression model based on a distribution in which zero inflated Poisson was added to the mixture of two Poissons. Thus the population distribution is now a mixture of three Poissons in which one component is concentrated on zero mass. We used the EM algorithm for estimating the regression parameters and detected the same variables with Kim et al's for significantly affecting the response. However, we could estimate the proportion of the fixed zero group to be 0.201, which was the characteristic of this model. We also noted that among two significant variables, the income and the volunteer experience(yes, no), the second variable could be utilized as a strategric variable for promoting the donation.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.22-31
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• 1995

본 논문에서는 포아송 반응을 갖는 로그 선형 회귀 모형에 붙스트랩 방법을 이용하여, 여러가지 통계적 추론을 위한 유용한 확률적 결과들을 연구.소개하고, 모의실험을 통한 소표본 성질들을 다양하게 제시하고자 한다. 특히 로그 선형 회귀 모형에 대한 최우 추정량 $\hat{\beta_n}$ 및 정보행렬 I(${\beta}_0$)의 추정량들 $I_1(\hat{\beta_n}{\cdot}X)$와 $I_2(\hat{\beta_n}{\cdot}X)$에 대한 일치성 및 정규성등의 확률적 성질들, 그리고 붙스트랩 방법을 적용한 대표본 성질들과 관련하여 여러가지 모의실험 결과들을 분석.연구하였다.

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

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.11a
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• pp.120-121
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• 2023

Recently, there has been a continuous imbalance between the demand and supply in the shipping industry. Consequently, shipping companies are implementing blank sailing to adjust the supply of vessels and achieve a balance between demand and supply. Blank sailing can create negative ripple effects by delaying cargo transportation, so this study uses Poisson regression analysis,

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.505-519
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• 2006

We consider zero-inflated count data, which is discrete count data but has too many zeroes compared to the Poisson distribution. Zero-inflated data can be found in various areas. Despite its increasing importance in practice, appropriate statistical inference on zero-inflated data is limited. Classical inference based on a large number theory does not fit unless the sample size is very large. And regular Poisson model shows lack of St due to many zeroes. To handle the difficulties, a mixture of distributions are considered for the zero-inflated data. Specifically, a mixture of a point mass at zero and a Poisson distribution is employed for the data. In addition, when there exist meaningful covariates selected to the response variable, loglinear link is used between the mean of the response and the covariates in the Poisson distribution part. We propose a Bayesian inference for the zero-inflated Poisson regression model by using a Markov Chain Monte Carlo method. We applied the proposed method to a Korean oral hygienic data and compared the inference results with other models. We found that the proposed method is superior in that it gives small parameter estimation error and more accurate predictions.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1087-1097
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• 2017

This study aims to find the best model to fit the number of insurance solicitor's turnovers of life insurance companies using count data regression models such as poisson regression, negative binomial regression, zero-inflated poisson regression, or zero-inflated negative binomial regression. Out of the four models, zero-inflated negative binomial model has been selected based on AIC and SBC criteria, which is due to over-dispersion and high proportion of zero-counts. The significant factors to affect insurance solicitor's turnover found to be a work period in current company, a total work period as financial planner, an affiliated corporation, and channel management satisfaction. We also have found that as the job satisfaction or the channel management satisfaction gets lower as channel management satisfaction, the number of insurance solicitor's turnovers increases. In addition, the total work period as financial planner has positive relationship with the number of insurance solicitor's turnovers, but the work period in current company has negative relationship with it.

• International Journal of Highway Engineering
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• v.11 no.2
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• pp.151-158
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• 2009

This study deals with the rear-end collisions in the rural aiea. The objectives of this study are 1) to analyze the characteristics of rear-end accidents of signalized intersections, and 2) to develop the accident models for Cheongju-Cheongwon. In pursing the above, this study gives the particular attentions to comparing the characters of urban and rural area. In this study, the dependent variables are the number of accidents and value of EPDO(equivalent property damage only), and independent variables are the traffic volumes and geometric elements. The main results analyzed are the followings. First, the statistical analyses show that the Poisson accident model using the number of accident as a dependant variable are statistically significant and the negative binomial accident model using the value of EPDO are statistically significant. Second, the independent variables of Poisson model are analyzed to be the ratio of high-occupancy vehicles, total traffic volume and the sum of exit/entry, and those of negative binomial regression are the main road width, total traffic volume and the ratio of high-occupancy vehicles. Finally, the specific independent variables to the rural area are the main road width, the ratio of high occupancy vehicle, and the sum exit/entry.