• 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.

• Journal of the Korean Society for information Management
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• v.16 no.3
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• pp.49-63
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• 1999

The purpose of this study is to investigate whether the terms in queries are distributed according to the 2-Poisson model in the documents represented by abstract/title or full-text. In this study, retrieval experiments using Binary independence and 2-Poisson independence model, which are based on the probabilistic theory, were conducted to see if the 2-Poisson distribution of the query terms has an influence on the retrieval effectiveness, particularly of full-text information retrieval system.

• Proceedings of the Korean Society for Information Management Conference
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• 1999.08a
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• pp.9-12
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• 1999

통계적 확률이론에 근거한 포아송 모형을 색인어 선정 기반으로 활용하고자 하는 2-포아송 함수와 3-포아송 함수 및 다중 포아송 함수에 대한 단계적 발전 과정을 살펴보았다. 아울러, 2-포아송이 한국어 문헌의 색인어 선정에 유용한지 알아보기 위해 한국어 말뭉치 데이터베이스 내 문헌 50개를 실험 대상으로 단어의 장서빈도와 문헌빈도를 이용하여 z값을 산출해 보았다.

• Journal of the Korean Society for information Management
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• v.17 no.1
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• pp.129-148
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• 2000

Experiments were performed on three subsets of a Korean test collection in order to determine whether 2-Poisson model's Z value is a good measure for selecting subject words from a document to be indexed. It was found that subject word selection based on the Z value was effective for only one subset with short texts, i.e., the Science and Technology subset. Correlation analyses between 2-Poisson model's Z and TF.IDF weight for the three subsets showed that the correlation was relatively high for two test subsets with short texts, i.e., the Science and Technology subset and the Newspaper subset.

• Environmental and Resource Economics Review
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• v.23 no.2
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• pp.331-347
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• 2014

The purpose of this study is to estimate the economic value of the recreational sea fishing in the Yellow Sea using count data model. For estimating consumer surplus, we used several count data model of travel cost recreation demand such as a poisson model(PM), a negative binomial model(NBM), a truncated poisson model(TPM), and a truncated negative binomial model(TNBM). Model results show that there is no exist the over-dispersion problem and a NBM was statistically more suitable than the other models. All parameters estimated are statistically significant and theoretically valid. The NBM was applied to estimate the travel demand and consumer surplus. The consumer surplus pre trip was estimated to be 254,453won, total consumer surplus per person and per year 1,536,896won.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.677-693
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• 2011

In this paper, we consider Bayesian approaches to zero inflated Poisson model, one of the popular models to analyze zero inflated count data. To generate posterior samples, we deal with a Markov Chain Monte Carlo method using a Gibbs sampler and an exact sampling method using an Inverse Bayes Formula(IBF). Posterior sampling algorithms using two methods are compared, and a convergence checking for a Gibbs sampler is discussed, in particular using posterior samples from IBF sampling. Based on these sampling methods, a real data analysis is performed for Trajan data (Marin et al., 1993) and our results are compared with existing Trajan data analysis. We also discuss model selection issues for Trajan data between the Poisson model and zero inflated Poisson model using various criteria. In addition, we complement the previous work by Rodrigues (2003) via further data analysis using a hierarchical Bayesian model.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.319-327
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• 1999

Zero-Inflated Poisson distributions have been widely used for defect-free products in manufacturing processes. It is very interesting to check the shift after the unknown changepoint. If the detectives are caused by the two different types of factor, we should use bivariate zero-inflated model. In this paper, likelihood ratio tests were used to detect the shift of changes after the changepoint. Some inferences for the parameters in this model were made.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.247-255
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• 1998

In this paper, a new subset selection problem in the Poisson model is considered under the normal predictors. It turns out that the subset model has bigger valiance than that of the Poisson model with random predictors and this has been used to derive new subset selection method similar to Mallows'$C_p$.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.22-31
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• 1995

본 논문에서는 포아송 반응을 갖는 로그 선형 회귀 모형에 붙스트랩 방법을 이용하여, 여러가지 통계적 추론을 위한 유용한 확률적 결과들을 연구.소개하고, 모의실험을 통한 소표본 성질들을 다양하게 제시하고자 한다. 특히 로그 선형 회귀 모형에 대한 최우 추정량 $\hat{\beta_n}$ 및 정보행렬 I(${\beta}_0$)의 추정량들 $I_1(\hat{\beta_n}{\cdot}X)$와 $I_2(\hat{\beta_n}{\cdot}X)$에 대한 일치성 및 정규성등의 확률적 성질들, 그리고 붙스트랩 방법을 적용한 대표본 성질들과 관련하여 여러가지 모의실험 결과들을 분석.연구하였다.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.287-301
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• 2018

It is common to encounter count data with excess zeros in various research fields such as the social sciences, natural sciences, medical science or engineering. Such count data have been explained mainly by zero-inflated Poisson model and extended models. Zero-inflated count data are also often correlated or clustered, in which random effects should be taken into account in the model. Frequentist approaches have been commonly used to fit such data. However, a Bayesian approach has advantages of prior information, avoidance of asymptotic approximations and practical estimation of the functions of parameters. We consider a Bayesian zero-inflated Poisson regression model with random effects for correlated zero-inflated count data. We conducted simulation studies to check the performance of the proposed model. We also applied the proposed model to smoking behavior data from the Regional Health Survey (2015) of the Korea Centers for disease control and prevention.