• Journal of Korean Society for Quality Management
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• v.33 no.1
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• pp.83-87
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• 2005

For the count response we normally consider Poisson regression model. However, the conventional fitting algorithm for Poisson regression model is not reliable at all when the response variable is measured with sizable contamination. In this study, we propose an alternative fitting algorithm that is resistant to outlying values in response and report a case study in semiconductor industry.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.271-276
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• 1997

A dynamic discount approach is proposed for the estimation of the Poisson parameter and the forecasting of the Poisson random variable, where the parameter of the Poisson distribution varies over time intervals. The recursive estimation procedure of the Poisson parameter is provided. Also the forecasted distribution of the Poisson random variable in the next time interval based on the information gathered until the current time interval is provided.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.527-538
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• 2019

This paper considers the issue of obtaining the optimal design in Poisson regression model when the sample size is small. Poisson regression model is widely used for the analysis of count data. Asymptotic theory provides the basis for making inference on the parameters in this model. However, for small size experiments, asymptotic approximations, such as unbiasedness, may not be valid. Therefore, first, we employ the second order expansion of the bias of the maximum likelihood estimator (MLE) and derive the mean square error (MSE) of MLE to measure the quality of an estimator. We then define DM-optimality criterion, which is based on a function of the MSE. This criterion is applied to obtain locally optimal designs for small size experiments. The effect of sample size on the obtained designs are shown. We also obtain locally DM-optimal designs for some special cases of the model.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1221-1229
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• 2014

In this paper we choose the best model among several bivariate Poisson models on Korean soccer data. The models considered allow for correlation between the number of goals of two competing teams. We use an R package called bivpois for bivariate Poisson regression models and the data of K-league for season 1983-2012. Finally we conclude that the best fitted model supported by the AIC and BIC is the bivariate Poisson model with constant covariance. The zero and diagonal inflated models did not improve the model fit. The model can be used to examine home-away effect, goodness of fit, attack and defense parameters.

• Journal of information and communication convergence engineering
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• v.7 no.1
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• pp.57-61
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• 2009

This paper has presented the subthreshold current model of FinFET using the potential variation in the doped channel based on the analytical solution of three dimensional Poisson's equation. The model has been verified by the comparison with the data from 3D numerical device simulator. The variation of subthreshold current with front and back gate bias has been studied. The variation of subthreshold swing and threshold voltage with front and back gate bias has been investigated.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1289-1297
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• 2012

We consider a general compound Poisson risk model in which the premium rate is surplus dependent. We analyze the joint distribution of the surplus immediately before ruin, the deffcit at ruin and the time of ruin by solving the integro-differential equation for the Gerber-Shiu discounted penalty function.

• 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'$C_p$.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1389-1397
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• 2016

Semi-parametric frailty model with nonparametric baseline hazards has been widely used for the analyses of clustered survival-time data. The frailty models can be fitted via an auxiliary Poisson hierarchical generalized linear model (HGLM). For the inferences of the frailty model marginal likelihood, which gives MLE, is often used. The marginal likelihood is usually obtained by integrating out random effects, but it often requires an intractable integration. In this paper, we propose to obtain the MLE via Laplace approximation using a Poisson HGLM approach for semi-parametric frailty model. The proposed HGLM approach uses hierarchical-likelihood (h-likelihood), which avoids integration itself. The proposed method is illustrated using a numerical study.

• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.1-9
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• 1998

In case of Zero-Inflated Poisson model with a change-point, likelihood ratio test statistic was used for testing hypothesis for a change-point. A change-point and several interesting parameters were estimated by using the method of moments and maximum likelihood. In order to compare the estimators, empirical mean-square-error was used. Real data for the Zero-Inflated Poisson model with a change-point and Poisson model without a change-point were examined.