• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.559-570
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• 1998

This paper introduces doubly Winsorized Poisson auto-model by truncating the support of a Poisson random variable both from above and below, and shows that this model has a same form of negpotential function as regular Poisson auto-model and one-way Winsorized Poisson auto-model. Strategies for maximum likelihood estimation of parameters are discussed. In addition to exact maximum likelihood estimation, Monte Carlo maximum likelihood estimation may be applied to this model.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.45-53
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• 2003

The Zero-Inflated Poisson regression is a model for count data with exess zeros. When the reponse variables have excess zeros, it is not easy to apply the Poisson regression model. In this paper, we study and simulate the zero-inflated Poisson regression model. An real example was applied to this model. Regression parameters are estimated by using MLE's. We also compare the fitness of zero-inflated Poisson model with the Poisson regression and decision tree model.

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.6
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• pp.642-647
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• 1998

A Poisson error model as a generalized linear mixed model (GLMM) has been suggested for genetic analysis of counted observations. One of the assumptions in this model is the normality for random effects. Since this assumption is not always appropriate, a more flexible model is needed. For count traits, a Poisson hierarchical generalized linear model (HGLM) that does not require the normality for random effects was proposed. In this paper, a Poisson-Gamma HGLM was examined along with corresponding analytical methods. While a difficulty arises with Poisson GLMM in making inferences to the expected values of observations, it can be avoided with the Poisson-Gamma HGLM. A numerical example with simulated embryo yield data is presented.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.793-798
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• 2008

Extreme value inference deals with fitting the generalized extreme value distribution model and the generalized Pareto distribution model, which are recently combined to give a single model, namely a two-dimensional non-homogeneous Poisson exceedance point process model. In this paper, we extend the two-dimensional non-homogeneous Poisson process model to include non-stationary effect or dependence on covariates and then derive the likelihood for the extended model.

• Journal of Biosystems Engineering
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• v.20 no.2
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• pp.133-140
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• 1995

The model of Poisson's ratio of the fruits was developed on the basis that the cylindrical fruits specimen became the barrel shape when it was being compressed. The model of the corrected creep compliance of the fruits was developed under considering the developed model of Poisson's ratio. Both of the Poisson's ratio and the corrected creep compliance of the samples showed the nonlinear viscoelastic behavior. Those models were a similar form, but their coefficients of the model were different, and these behaviors of the samples were well described by the nonlinear model as a function of the initial stress and time. Effects of storage condition and period on the Poisson's ratio of the samples were investigated, and comparisons between the corrected and the uncorrected creep compliance of the samples were made.

• The Korean Journal of Community Living Science
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• v.22 no.2
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• pp.247-255
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• 2011

This study analyzed the demand for a community-based tourism site using a poisson model, a negative binominal model, a truncated poisson model and a truncated negative binominal model as count data models. For these reasons, questionnaire surveys were conducted into 5 community-based tourism sites in Chuncheon city with 406 tourists, and was analyzed using the STATA program. The fitness levels of four models were significant(p=0.0000) using a likelihood ratio test. The study results suggest that the demand of community-based tourism sites for visiting tourists was influenced by a pre-visiting experience, recognition of sustainable tourism, visitation of downtown, purchase of souvenir or farm produce, conversation with regional residents, regional harmony, preservation of natural resources and sex within the poisson and truncated poisson models. However, the variables of visitation of downtown, preservation of natural resources and sex were not significant within the negative binominal model and the visitation of downtown and preservation of natural resources were not significant within the truncated negative binominal model. The results of the visiting demand of community-based tourism sites can provide information for sustainable regional development strategies.

• Journal of information and communication convergence engineering
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• v.7 no.3
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• pp.361-365
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• 2009

This paper has been presented the transport characteristics of FinFET using the analytical potential model based on the Poisson's equation in subthreshold and threshold region. The threshold voltage is the most important factor of device design since threshold voltage decides ON/OFF of transistor. We have investigated the variations of threshold voltage and drain induced barrier lowing according to the variation of geometry such as the length, width and thickness of channel. The analytical potential model derived from the three dimensional Poisson's equation has been used since the channel electrostatics under threshold and subthreshold region is governed by the Poisson's equation. The appropriate boundary conditions for source/drain and gates has been also used to solve analytically the three dimensional Poisson's equation. Since the model is validated by comparing with the three dimensional numerical simulation, the subthreshold current is derived from this potential model. The threshold voltage is obtained from calculating the front gate bias when the drain current is $10^{-6}A$.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.791-801
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• 2010

This study reviews the concept of disclosure, disclosure risks, and uniqueness. The number of uniqueness in the population is of great importance in evaluating the disclosure risk of micro data files. We approach this problem by considering some basic superpopulation models including the Multinomial-Dirichlet model, the Poisson- Gamma model of Bethlehem et al. (1990) and Takemura (1997), and the Modified Multinomial-Dirichlet model. We decided the critical population size of each superpopulation model for four different superpopulation models.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.177-186
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• 2003

The Zero-Inflated Poisson regression is a model for count data with exess zeros. When the correlated response variables are intrested, we have to extend the univariate zero-inflated regression model to multivariate model. In this paper, we study and simulate the multivariate zero-inflated regression model. A real example was applied to this model. Regression parameters are estimated by using MLE's. We also compare the fitness of multivariate zero-inflated Poisson regression model with the decision tree model.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.505-519
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• 2006

We consider zero-inflated count data, which is discrete count data but has too many zeroes compared to the Poisson distribution. Zero-inflated data can be found in various areas. Despite its increasing importance in practice, appropriate statistical inference on zero-inflated data is limited. Classical inference based on a large number theory does not fit unless the sample size is very large. And regular Poisson model shows lack of St due to many zeroes. To handle the difficulties, a mixture of distributions are considered for the zero-inflated data. Specifically, a mixture of a point mass at zero and a Poisson distribution is employed for the data. In addition, when there exist meaningful covariates selected to the response variable, loglinear link is used between the mean of the response and the covariates in the Poisson distribution part. We propose a Bayesian inference for the zero-inflated Poisson regression model by using a Markov Chain Monte Carlo method. We applied the proposed method to a Korean oral hygienic data and compared the inference results with other models. We found that the proposed method is superior in that it gives small parameter estimation error and more accurate predictions.