• Journal of the Korean Regional Science Association
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• v.31 no.3
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• pp.39-54
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• 2015

To assess an economic value of Cheonggyecheon river restoration project, an in-depth exit survey data was collected to apply travel cost method in this study. Poisson model, Negative Binomial, Zero-truncated Poisson, and Zero-truncated Negative Binomial model were executed due to the nature of count data. Empirical results showed that regressors were statistically significant and corresponded to general consumer theory. Since our survey data showed over-dispersion, Zero-truncated Negative Binomial was selected as an optimal one to analyze travel demand of Cheonggyecheon by model goodness of fit test among those aforementioned empirical models. Estimating an economic value of Cheonggyecheon river restoration project, which is known as an ecological river restoration project, we used annual visit of individual traveler and an optimal model. Suffice to say that the annual economic value of Cheonggyecheon river restoration project was estimated as 193.4 billion won in 2013.

• 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 Korean Society of Transportation
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• v.33 no.6
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• pp.554-563
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• 2015

This study dealt with developing an accident model for rural signalized intersections with random parameter negative binomial method. The limitation of previous count models(especially, Poisson/Negative Binomial model) is not to explain the integrated variations in terms of time and the distinctive characters a specific point/segment has. This drawback of the traditional count models results in the underestimation of the standard error(t-value inflation) of the derived coefficient and finally affects the low-reliability of the whole model. To solve this problem, this study improves the limitation of traditional count models by suggesting the use of random parameter which takes account of heterogeneity of each point/segment. Through the analyses, it was found that the increase of traffic flow and pedestrian facilities on minor streets had positive effects on the increase of traffic accidents. Left turning lanes and median on major streets reduced the number of accidents. The analysis results show that the random parameter modeling is an effective method for investigating the influence on traffic accident from road geometries. However, this study could not analyze the effects of sequential changes of driving conditions including geometries and safety facilities.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.837-849
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• 2019

Latent class models (LCM) are useful tools to draw hidden information from categorical data. This model can also be interpreted as a mixture model with multinomial component distributions. In some cases, however, an available dataset may contain both categorical and count or continuous data. For such cases, we can extend the LCM to a mixture model with both multinomial and other component distributions such as normal and Poisson distributions. In this paper, we consider a LCM for the data containing categorical and count data to analyze the Drug Review dataset which contains categorical responses and text review. From this data analysis, we show that we can obtain more specific hidden inforamtion than those from the LCM only with categorical responses.

• The Korean Journal of Applied Statistics
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• v.8 no.2
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• pp.147-161
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• 1995

The primary objective of this paper is to review parametric models and test statistics related to overdspersion of count data. Poisson or binomial assumption often fails to explain overdispersion. We reviewed real examples of overdispersion in count data that occurred in toxicological or teratological experiments. We also reviewed several models that were suggested for implementing experiments. We also reviewed several models that were suggested for implementing the extra-binomial variation or hyper-Poisson variability, and we noted how these models were generalized and further developed. The approaches that have been suggested for the overdispersion fall into two broad categories. The one is to develop a parametric model for it, and the other is to assume a particular relationship between the variance and the mean of the response variable and to derive a score test staistics for detecting the overdispersion. Recently, Dean(1992) derived a general score test statistics for detecting overdispersion from the exponential family.

• Journal of Korea Technology Innovation Society
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• v.13 no.2
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• pp.365-378
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• 2010

For sustainable economic growth, the Korea government continues to increase its investment in R&D and at the same time, have reinforced the management of R&D outcomes and enlargement of its infrastructure for utilization. However, a strategic R&D planning that draws an outstanding outcomes is more important than the management of its post-outcomes. The purpose of this study is to propose an implication on the policy of R&D planning for obtaining a good R&D outcomes such as a high quality patent. We compared and analyzed the determinants of the patent quality in fuel cell and solar cell technology using the count data models. Estimated results showed that the determinants of the patent quality in technologies were different from each other. Therefore, having appropriate R&D planning strategies for all technologies was more effective than applying one same strategy for all because of technological differences, and based on the estimated results, we suggested the R&D planning strategy in fuel cell and solar cell technologies that could in result in high R&D outcomes.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.445-459
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• 2014

As a certain job is repeatedly done by a worker, the outcome comparative to the effort to complete the job gets more remarkable. The outcome may be the time required and fraction defective. This phenomenon is referred to a learning-curve effect. We focus on the parametric modeling of the learning-curve effects on count data using a logistic cumulative distribution function and some probability mass functions such as a Poisson and negative binomial. We conduct various simulation scenarios to clarify the characteristics of the proposed model. We also consider a real application to compare the two discrete-type distribution functions.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.335-344
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• 2010

Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.

• 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.13 no.2
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• pp.541-562
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• 2000

The generalized linear mixed model framework is for handling count-type categorical data as well as for clustered or overdispersed non-Gaussian data, or for non-linear model data. In this study, we review its general formulation and estimation methods, based on quasi-likelihood and Monte-Carlo techniques. The current research areas and topics for further development are also mentioned.