• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.231-239
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• 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.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.311-325
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• 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
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• v.27 no.6
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• pp.923-932
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• 2014

The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1459-1473
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• 2016

Both longitudinal data and survival data are collected simultaneously in longitudinal data which are observed throughout the passage of time. In this case, the effect of the independent variable becomes biased (provided that sole use of longitudinal data analysis does not consider the relation between both data used) if the missing that occurred in the longitudinal data is non-ignorable because it is caused by a correlation with the survival data. A joint model of longitudinal data and survival data was studied as a solution for such problem in order to obtain an unbiased result by considering the survival model for the cause of missing. In this paper, a joint model of the longitudinal zero-inflated count data and survival data is studied by replacing the longitudinal part with zero-inflated count data. A hurdle model and proportional hazards model were used for each longitudinal zero inflated count data and survival data; in addition, both sub-models were linked based on the assumption that the random effect of sub-models follow the multivariate normal distribution. We used the EM algorithm for the maximum likelihood estimator of parameters and estimated standard errors of parameters were calculated using the profile likelihood method. In simulation, we observed a better performance of the joint model in bias and coverage probability compared to the separate model.

• 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.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.413-421
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• 2010

In this study, we considered bivariate conditional auto-regressive model taking into account spatial association as well as correlation between the two dependent variables, which are the counts of murder and burglary. We conducted likelihood ratio test for checking over-dispersion issues prior to applying spatial poisson models. For the real application, we used the annual counts of violent crimes at 25 districts of Seoul in 2007. The statistical results are visually illustrated by geographical information system.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.105-119
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• 1997

Barnwal and Paul(1988) derived the likelihood ratio statistic and $C(\alpha)$ statistic for testing the equality of the means of several groups of count data in the presence of a common dispersion parameter. These tests are generalized to be applicable without the restriction of a common dispersion parameter. And the assumed model of data is also extended from negative binomial to double exponential Poisson model. Monte Carlo simulations show the superiority of $C(\alpha)$ statistic based on the double exponential Poisson family which has a very simple form and requires estimates of the parameters only under the null hypothesis.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.4
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• pp.26-36
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• 2016

This study aims to identify the place and location of Gasan around Cheonggyecheon in the Joseon Dynasty, and also to examine the current status of their changes or disappearance in the process of modernization. On top of literature research on old document and maps related to dredging of Cheonggyecheon, the field survey to understand the actual status and location of Gasan was conducted. The study contents are like below. First, through old document and maps related to Cheonggyecheon in the Joseon Dynasty, the contents and records related to Gasan around Cheonggyecheon were selected. Such Gasan were mainly built up by dredging work of branch/streamlet in the era of King Sejong, and also the large-scale dredging during the period of King Yeongjo. Second, the location of Gasan and forest around Cheonggyecheon shown in old maps was understood. With Ogansumun(Five-arched Floodgates) as the center, they were constructed on both hills of Cheonggyecheon inside/outside of the capital city, and its surrounding branches. Third, based on the measures to create a scale map based on Susunjeondo, the location of Gasan on old maps was found on the current map, and then the current status of places expressed as Gasan was surveyed. In the results of the study, even though most of the Gasan built up around Cheonggyecheon in the Joseon Dynasty have disappeared by the urban development performed since the end of 19th century, still, there are some remains left.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.380-380
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• 2021

수위관측소에서는 일반적으로 실시간 수위관측을 통해 연속된 수위자료가 계측이 되고 있는 반면, 하천의 유량은 실시간 측정이 어렵기 때문에 목적에 따라 시기 또는 수위별로 측정을 실시하고 있다. 이를 통해 확보한 유량자료를 이용하여 수위-유량관계곡선(Stage-Discharge relationship)을 개발하고, 이 곡선을 이용하여 연속적인 유량자료를 제시하고 있다. 자연하천의 경우 하도의 인공적 및 자연적 변화에 따라 수위-유량관계가 변화하게 된다. 특히 합류부에 위치한 관측소는 일반적인 단일 하천에서의 수위-유량관계와 다르게 배수(Backwater)가 발생한다. 이는 등류수위의 경사보다 완경사가 발생하고 단순 수위-유량 일대일 관계를 나타내는데 어려움이 있다. 따라서 수위-유량관계곡선을 이용한 유량환산에 있어 배수 발생기간은 왜곡된 유량자료를 생산하거나 유량환산 불가기간이 발생하는 문제점이 있다. 미호천의 지류인 한천에 위치한 진천군(인산리) 관측소는 하류 약 500m에서 미호천 본류와 합류하고 있다. 또한 합류점을 기준으로 본류인 미호천 상류 약 3km에 진천군(가산교) 관측소, 하류 약 2km에 진천군(오갑교) 관측소가 위치하고 있다. 따라서 호우사상의 크기에 따라 본류 배수영향으로 진천군(인산리) 관측소 중고수위에서 수위-유량관계곡선으로 산정된 유량의 크기가 과대 산정되어 진천군(가산교)와 합산한 유량이 진천군(오갑교) 관측소 유량과 상하류 역전이 발생한다. 이러한 문제를 해결하기 위해 합류부 보조수위계 설치를 통해 배수영향을 검토 할 계획이었으나 2020년 큰 호우사상으로 보조수위계가 유실되어 본류에 위치한 진천군(가산교)와 진천군(오갑교) 관측소의 연속적인 수위, 유량, 유속 등 수리학적 인자를 이용하여 에너지 방정식으로부터 합류부 수위를 산정하고 지류인 한천의 배수영향 검토를 통해 진천군(인산리) 관측소의 배수 발생기간을 검토하였다. 따라서 수위-유량관계곡선을 이용한 유량환산에 있어서 배수에 의한 왜곡된 유량 자료를 제외하였으나 배수에 의한 유량환산 불가기간은 추후 해결해야 할 과제로 남는다.