• Journal of Korea Water Resources Association
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• v.49 no.6
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• pp.481-493
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• 2016

The statistical analysis to the torrential rainfall data that is defined as a rainfall amount more than 80 mm/day is performed with Daegu and Busan rainfall data which is collected during 384 months. The number of occurrence of the torrential rainfall events can be simulated usually using Poisson distribution. However, the Poisson distribution can be frequently failed to simulate the statistical characteristics of the observed value when the observed data is zero-inflated. Therefore, in this study, Generalized Poisson distribution (GPD), Zero-Inflated Poisson distribution (ZIP), Zero-Inflated Generalized Poisson distribution (ZIGP), and Bayesian ZIGP model were used to resolve the zero-inflated problem in the torrential rainfall data. Especially, in Bayesian ZIGP model, a informative prior distribution was used to increase the accuracy of that model. Finally, it was suggested that POI and GPD model should be discouraged to fit the frequency of the torrential rainfall data. Also, Bayesian ZIGP model using informative prior provided the most accurate results. Additionally, it was recommended that ZIP model could be alternative choice on the practical aspect since the Bayesian approach of this study was considerably complex.

• 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.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2841-2852
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• 2018

This study examines regression modeling method using zero-inflated distribution in relation to estimation of exposure distribution required in advertisement media planning. Exposure distribution is the percentage of audiences that are exposed each time the ad is repeated. Such an exposure distribution plays a very important role in providing basic information necessary for calculating various indicators for quantitatively measuring the advertising effect. Especially, due to the decrease of advertising price and the spread of various media, the frequency of the advertisement or the broadcasting of specific advertisements has been greatly increased compared to the past. As a result, the frequency of exposure is relatively decreasing. In this situation, the number of individuals who are not exposed to the media, that is, are not exposed to advertising structurally is increasing. This research proposes advertising exposure distribution models using a zero-inflated regression model, and conducts a comparative study using actual cases.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.471-488
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• 2018

In this paper, we propose extending proportional hazards frailty models to allow a discrete distribution for the frailty variable. Having zero frailty can be interpreted as being immune or cured. Thus, we develop a new survival model induced by discrete frailty with zero-inflated power series distribution, which can account for overdispersion. This proposal also allows for a realistic description of non-risk individuals, since individuals cured due to intrinsic factors (immunes) are modeled by a deterministic fraction of zero-risk while those cured due to an intervention are modeled by a random fraction. We put the proposed model in a Bayesian framework and use a Markov chain Monte Carlo algorithm for the computation of posterior distribution. A simulation study is conducted to assess the proposed model and the computation algorithm. We also discuss model selection based on pseudo-Bayes factors as well as developing case influence diagnostics for the joint posterior distribution through ${\psi}-divergence$ measures. The motivating cutaneous melanoma data is analyzed for illustration purposes.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.247-253
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• 1998

In ease of the epidemic Zero-Inflated Poisson model, likelihood ratio test was used for testing epidemic alternatives. Epidemic changepoints were estimated by the method of least squares. It were used for starting points to estimate the maximum likelihood estimators. And several parameters were compared through the Monte Carlo simulations. As a result, maximum likelihood estimators for the epidemic chaagepoints and several parameters are better than the least squares and moment estimators.

• Journal of the Korean Society of Safety
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• v.29 no.6
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• pp.151-157
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• 2014

This study deals with the rear-end collision at roundabouts. The purpose of this study is to develop the accident models of rear-end collision in Korea. In pursuing the above, this study gives particular attention to developing the appropriate models using Poisson, negative binomial model, ZAM, multiple linear and nonlinear regression models, and statistical analysis tools. The main results are as follows. First, the Vuong statistics and overdispersion parameters indicate that ZIP is the most appropriate model among count data models. Second, RMSE, MPB, MAD and correlation coefficient tests show that the multiple nonlinear model is the most suitable to the rear-end collision data. Finally, such the independent variables as traffic volume, ratio of heavy vehicle, number of circulatory roadway lane, number of crosswalk and stop line are adopted in the optimal model.

• The Journal of Information Systems
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• v.23 no.4
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• pp.169-196
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• 2014

This study investigates how information and communication technology (ICT) can damage intellectual property rights (IPR) in the movie industry. Utilizing a survey questionnaire to gather information about the extensive use of ICT devices, including tablet PCs and smartphones, we demonstrate how digital piracy behavior is associated with various socio-demographic characteristics. Econometrically, since a large number of people do not engage in piracy activities, we adopt a zero-inflated negative binomial model. We find that people with tablet PCs are more likely to engage in the piracy of movies from peer-to-peer (P2P) sites. In particular, when we categorize ICT devices based on whether they are portable and allow downloads, we find that people with devices equipped with both functions are most likely to engage in movie piracy.

• Journal of Korean Academy of Nursing
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• v.50 no.5
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• pp.658-670
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• 2020

Purpose: This study aimed to identify the predictors of blood and body fluid exposure (BBFE) in multifaceted individual (sleep disturbance and fatigue), occupational (occupational stress), and organizational (hospital safety climate) factors, as well as infection prevention behavior. We also aimed to test the mediating effect of infection prevention behavior in relation to multifaceted factors and the frequency of BBFE. Methods: This study was based on a secondary data analysis, using data of 246 nurses from the Shift Work Nurses' Health and Turnover study. Based on the characteristics of zero-inflated and over-dispersed count data of frequencies of BBFE, the data were analyzed to calculate zero-inflated negative binomial regression within a generalized linear model and to test the mediating effect using SPSS 25.0, Stata 14.1, and PROCESS macro. Results: We found that the frequency of BBFE increased in subjects with disturbed sleep (IRR = 1.87, p = .049), and the probability of non-BBFE increased in subjects showing higher infection prevention behavior (IRR = 15.05, p = .006) and a hospital safety climate (IRR = 28.46, p = .018). We also found that infection prevention behavior had mediating effects on the occupational stress-BBFE and hospital safety climate-BBFE relationships. Conclusion: Sleep disturbance is an important risk factor related to frequency of BBFE, whereas preventive factors are infection prevention behavior and hospital safety climate. We suggest individual and systemic efforts to improve sleep, occupational stress, and hospital safety climate to prevent BBFE occurrence.

• 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 Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.923-932
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• 2013

This paper proposes a method to predict the number of foodborne disease outbreaks by microbes. The weekly data of food poisoning occurrences by microbes in Korea contain many zero-valued observations and have dependency between outbreaks. In order to model both phenomena, the number of food poisonings is predicted by an autoregressive model and the probabilities of food poisoning occurrences by microbes (given the total of food poisonings) are estimated by the baseline category logit model. The predicted number of foodborne disease outbreaks by a microbe is obtained by multiplying the predicted number of foodborne disease outbreaks and the estimated probability of the food poisoning by the corresponding microbe. The mean squared error and the mean absolute value error are evaluated to compare the performances of the proposed method and the zero-inflated model.