• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.733-749
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• 2018

Sample selection arises as a result of the partial observability of the outcome of interest in a study. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. Recently sample selection models for binomial and Poisson response variables have been proposed. Based on the theory of symmetry-modulated distribution, we extend these to a model for overdispersed count data. This type of data with no sample selection is often modeled using negative binomial distribution. Hence we propose a sample selection model for overdispersed count data using the negative binomial distribution. A real data application is employed. Simulation studies reveal that our estimation method based on profile log-likelihood is stable.

• Annals of Occupational and Environmental Medicine
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• v.34
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• pp.39.1-39.11
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• 2022

Background: Workers in logistics centers are always pressed for time to collect and pack products. They also participate in high-intensity manual labor in which various musculoskeletal hazards exist. In the case of logistic center labor, it is estimated that there is a high risk of presenteeism due to the above characteristics which can cause deterioration of workers' mental health. However, there is insufficient research on this topic. Methods: Workers in a logistic center were surveyed using an Internet questionnaire. The survey items included demographic characteristics, labor intensity and work-related factors, and mental health aspects such as depression and anxiety. The survey was conducted for about a month from July 26, 2021 and a total of 353 people were analyzed. Through the χ² test and t-test, the characteristics of workers who experienced presenteeism were examined and the prevalence ratios (PRs) of depression and anxiety experiences were calculated by multivariable Poisson regression. Afterwards, stratification analysis considering gender, the type of contract, and labor intensity was implemented. Results: In the group that experienced presenteeism, the number of working days per week was higher and fixed-term workers, high labor intensity, and sleep deprivation were more common. In the multi-Poisson regression analysis conducted by adjusting the demographic characteristics, working hours, and work-related factors, the PRs of depression and anxiety were 1.98 (95% confidence interval: 1.24-3.18) and 1.81 (1.22-2.68), respectively. In particular, the p-value for interactions was significant when stratified with the type of contract. Conclusions: As a result of the study, presenteeism and mental health were associated in logistic center workers. To prevent mental health issues of logistic center workers, management of presenteeism is necessary and a prospective study is needed.

• International Journal of Highway Engineering
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• v.16 no.2
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• pp.119-129
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• 2014

PURPOSES : The purpose of this study is to develop the U-turn accident model at 4-legged signalized intersections in urban areas. METHODS : In order to analyze the characteristics of the accidents which are associated with U-turn operation at 4-legged signalized intersections in urban areas and develop an U-turn accident model by regression analysis, the tests of overdispersion and zero-inflation are conducted about the dependent variables of number of accidents and EPDO (Equivalent Property Damage Only). RESULTS : As their results, the Poisson model fits best for number of accident and the ZIP (Zero Inflated Poisson) fits best for EPOD, the variables of conflict traffic, width of opposing road, traffic passing speed are adopted as independent variable for both models. The variables of number of bus berths and rate of U-turn signal time at which the U-turn is permitted are adopted as independent variable only for EPDO. CONCLUSIONS : These study results suggest that U-turn would be permitted at the intersection where the width of opposing road is wider than 11.9 meters, the passing vehicle speed is not high and U-turn operation is not hindered by the buses stopping at bus stops.

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

• Journal of East-West Nursing Research
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• v.28 no.2
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• pp.160-169
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• 2022

Purpose: This study was conducted to identify predictors related to aggressive behavior of patients with mental illness admitted to a closed psychiatric ward. Methods: This study adopted a retrospective design which analyzed the hospital medical records of 363 patients with mental illness admitted to the psychiatric closed ward of a university hospital in Seoul, Korea. The collected data were analyzed using SPSS IBM 20.0 and STATA 12.0 SE. ZIP (Zero-Inflated Poisson) and count data analysis were used for the factor influencing the occurrence and frequency of aggressive behavior. Results: The results of ZIP model showed that the factors influencing non-probability of aggressive behavior were anxiety, non-adherence, and frustration. In addition, the factors influencing frequency of aggressive behavior were bipolar disorder and personality disorder trait. Conclusion: We found that bipolar disorder, frustration, and non-adherence are more likely to increase the likelihood of aggressive behavior in patients with mental illness. In particular, patients diagnosed with bipolar disorder were 1.95 times more likely to engage in repetitive aggressive behavior compared to those without a diagnose. However, since the results were different form previous studies, further studies on the traits of anxiety and personality disorders are needed.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.375-388
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• 2021

This paper aims to help prevent the spread of COVID-19 by analyzing confirmed cases of COVID-19 in Daejeon. A high volume of visitors, downtown areas, and psychological fatigue with prolonged social distancing were considered as risk factors associated with the spread of COVID-19. We considered the weekly confirmed cases in each administrative district as a response variable. Explanatory variables were the number of passengers getting off at a bus station in each administrative district and the elapsed time since the Korean government had imposed distancing in daily life. We employed a mixed-effects zero-inflated Poisson regression model because the number of cases was repeatedly measured with excess zero-count data. We conducted k-means clustering to identify three groups of administrative districts having different characteristics in terms of the number of bars, the population size, and the distance to the closest college. Considering that the number of confirmed cases might vary depending on districts' characteristics, the clustering information was incorporated as a categorical explanatory variable. We found that Covid-19 was more prevalent as population size increased and a district is downtown. As the number of passengers getting off at a downtown district increased, the confirmed cases significantly increased.

• Child Health Nursing Research
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• v.13 no.3
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• pp.318-328
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• 2007

Purpose: Because it is important to prevent adolescents from becoming involved in smoking, this study was done to explore important factors influencing cigarette use and the increase in smoking frequency. Method: For this study the Korea Youth Panel Survey (KYPS) was analyzed. Because the KYPS is longitudinal, a fixed effect regression method was used to control for the effects of time-independent factors. More specifically, a logistic regression was used to explore factors affecting cigarette use, and a Poisson regression was used to explore smoking frequency. Result: As the adolescents got older, the number of male adolescents who tried smoking increased, while the number of female adolescents who tried smoking decreased. Also, the frequency of cigarette use among male and female smokers increased over time. Significant factors affecting cigarette use were friends who smoked, delinquent behavior, and loneliness at schools. Important factors affecting the increase in smoking frequency were grade (e.g., the 2nd- and 3rd-year of middle school), friends who smoked, delinquent behavior, monthly pocket money, expectation for the highest level of education, and attack tendency. Conclusions: To solve the problems linked to adolescent smoking, it is critical to develop intervention programs that target specific homogeneous subgroups of smokers, and that take into consideration gender difference in smoking and factors affecting levels of smoking behavior.

• Journal of Korean Society of Transportation
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• v.17 no.5
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• pp.123-135
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• 1999

The rapid increase of Passenger cars which is caused by the discomfort of Public transit and the Preference of automobiles is the major factor of increasing traffic congestions in Seoul With the point that leading the automobilists to the Public transit can be the most important Policy to ease these traffic congestions, this study focuses on the behavioral aspects of company employees and university students and investigates factors influencing bus ridership. To be brief, by estimating bus ridership through count models, this study investigates factors which influence bus ridership and elicits Political suggestions which lead automobilists to Public transit. The Purpose in this study is the application of appropriate count data model. The count data models have been widely applied to the economic area from the middle of the 1980s and to transportation aspect mainly in the foreign countries from the latter half of the 1980s. Even though a few studies in this country employed count data model to count data. all of them were Poisson regression models without suitable tests for the importance of the model specification. In the end, as the result of statistical test, negative binomial regression model which is suitable for overdispersed data was found to be appropriate for the data of weekly bus ridership. To emphasize the importance of model specification, both of poisson regression model and negative binomial regression model were estimated and the results were compared.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1511-1520
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• 2017

The purpose of this study is to analyze military combat training data to improve military operation and training methods and verify required military doctrine. We set the number of combat disabled enemies, which the individual combatants make using their weapons, as the response variable regarding offensive operations from scientific military training data of reinforced infantry battalion. Our response variable has more zero observations than would be allowed for by the traditional GLM such as Poisson regression. We used the zero-inflated regression and the hurdle regression for data analysis considering the over-dispersion and excessive zero observation problems. Our result can be utilized as an appropriate reference in order to verify a military doctrine for small units and analysis of various operational and tactical factors.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.951-961
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• 2011

In this paper, we propose a Bayesian inference using the Markov Chain Monte Carlo(MCMC) method for the zero inflated negative binomial(ZINB) regression model. The proposed model allows the regression model for zero inflation probability as well as the regression model for the mean of the dependent variable. This extends the work of Jang et al. (2010) to the fully defiend ZINB regression model. In addition, we apply the proposed method to a real data example, and compare the efficiency with the zero inflated Poisson model using the DIC. Since the DIC of the ZINB is smaller than that of the ZIP, the ZINB model shows superior performance over the ZIP model in zero inflated count data with overdispersion.