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

• The Korean Journal of Applied Statistics
• /
• v.35 no.2
• /
• pp.311-325
• /
• 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 Korean Journal of Applied Statistics
• /
• v.28 no.3
• /
• pp.583-592
• /
• 2015

Zero-inflation has recently attracted much attention in integer-valued time series. This article deals with conditional variance (volatility) modeling for the zero-inflated count time series. We incorporate zero-inflation property into integer-valued GARCH (INGARCH) via conditional Poisson and negative binomial marginals. The Cholera frequency time series is analyzed as a data application. Estimation is carried out using EM-algorithm as suggested by Zhu (2012).

• Communications for Statistical Applications and Methods
• /
• v.18 no.5
• /
• pp.571-579
• /
• 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 Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.231-239
• /
• 2015

A Poisson model is the first choice for counts data. Quasi Poisson or negative binomial models are usually used in cases of over (or under) dispersed data. However, these models might be unsuitable if the data consist of excessive number of zeros (zero inflated data). For zero inflated counts data, Zero Inflated Poisson (ZIP) or Zero Inflated Negative Binomial (ZINB) models are recommended to address the issue. In this paper, we further considered a situation where zero inflated data are spatially correlated. A mixed effect model with random effects that account for spatial autocorrelation is used to fit the data.

• Environmental and Resource Economics Review
• /
• v.27 no.3
• /
• pp.545-567
• /
• 2018

This study applied and compared Poisson model, negative binomial model, zero inflated Poisson model, and zero inflated negative binomial model to estimate determinants of employed labour quantity. To estimate each of models, this study used fisheries census data which were obtained at microdata integrated service running by Statistics Korea. The study selected zero inflated negative binomial model according to the Vuong test and Likelihood-ratio test. In addition, the study estimated fishing village's practical changes on employed labour quantity as analyzing changes from 2010 to 2015. The results showed that the household with fishing vessels and high selling price had a significant effect on decrease of the labour quantities. Meanwhile, the longer work experience of the household, the more significant the increase in the labour quantities. In conclusion, this study presented that capitalized fishing household and the acceleration of aging had a significant impact on the change in the labour quantities.

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

• The Korean Journal of Applied Statistics
• /
• v.37 no.4
• /
• pp.499 -508
• /
• 2024

In this paper, we investigate hierarchical time series forecasting that adhere to a hierarchical structure when deriving predicted values by analyzing segmented data as well as aggregated datasets. The occurrences of food poisoning by a specific pathogen are analyzed using zero-inflated Poisson regression models and negative binomial regression models. The occurrences of major, miscellaneous, and overall food poisoning are analyzed using Poisson regression models and negative binomial regression models. For hierarchical time series forecasting, the MinT estimation proposed by Wickramasuriya et al. (2019) is employed. Negative predicted values resulting from hierarchical adjustments are adjusted to zero, and weights are multiplied to the remaining lowest-level variables to satisfy the hierarchical structure. Empirical analysis revealed that there is little difference between hierarchical and non-hierarchical adjustments in predictions based on pathogens. However, hierarchical adjustments generally yield superior results for predictions concerning major, miscellaneous, and overall occurrences. Without hierarchical adjustment, instances may occur where the predicted frequencies of the lowest-level variables exceed that of major or miscellaneous occurrences. However, the proposed method enables the acquisition of predictions that adhere to the hierarchical structure.

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

• Journal of the Korean Data Analysis Society
• /
• v.20 no.6
• /
• pp.2829-2840
• /
• 2018

The random variable with an arbitrary value or more is called semi-continuous variable or zero-inflated one in case that its boundary value is more frequently observed than expected. This means the boundary value is likely to be practically observed more than it should be theoretically under certain probability distribution. When the distribution considered is continuous, the variable is defined as semi-continuous and when one of discrete distribution is assumed for the variable, we regard it as zero-inflated. In this study, we introduce the two-part model, which consists of one part for modelling the binary response and the other part for modelling the variable greater than the boundary value. Especially, the zero-inflated regression models are explained by using Poisson distribution and negative binomial distribution. In real data analysis, we employ the zero-inflated regression models to estimate the number of days under extreme heat-wave circumstances during the last 10 years in South Korea. Based on the estimation results, we create prediction maps for the estimated number of days under heat-wave advisory and heat-wave warning by using the universal kriging, which is one of the spatial prediction methods.