• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.733-742
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• 1998

For industrial and medical lifetime data, the generalized log-gamma regression model is considered. Then the Bayesian analysis for the generalized log-gamma regression with censored data are explained and following the data augmentation (Tanner and Wang; 1987), the censored data is replaced by simulated data. To overcome the complicated Bayesian computation, Makov Chain Monte Carlo (MCMC) method is employed. Then some modified algorithms are proposed to implement MCMC. Finally, one example is presented.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.13-18
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• 2005

We explore the structure and usefulness of the 3-D residual plot as a basic tool for dealing with interaction in generalized linear models. If predictors have an interaction effect, the shape obtained by rotating the 3-D residual plot will show its presence. To illustrate the use of this plot as an aid to exploring the interaction, we present an example of a binomial regression model using simulated data.

• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.63-71
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• 1995

We have consider the study of local influence for smoothing parameter estimates in spline regression model with heteroscedasticity. Practically, generalized cross-validation does not work well in the presence of heteroscedasticity. Thus we have proposed the local influence measure for generalized cross-validation estimates when errors are heteroscedastic. And we have examined effects of diagnostic by above measures through Hyperinflation data.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.337-346
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• 2004

The Pearson chi-square goodness-of-fit test and the likelihood ratio tests are usually used for testing independence in two-way contingency tables under random sampling. But both of these tests may provide false results for the contingency table with clustered observations. In this case we consider the generalized linear mixed model which includes random effects of clustering in addition to the fixed effects of covariates. Both the heterogeneity between clusters and the dependency within a cluster can be explained via generalized linear mixed model. In this paper we introduce several types of generalized linear mixed model for testing independence in contingency tables with clustered observations. We also discuss the fitting of these models through a real dataset.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.617-631
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• 2000

This paper is concerned with suggesting a Bayesian method for variable selection in generalized logit model. It is based on Laplace-Metropolis algorithm intended to propose a simple method for estimating the marginal likelihood of the model. The algorithm then leads to a criterion for the selection of variables. The criterion is to find a subset of variables that maximizes the marginal likelihood of the model and it is seen to be a Bayes rule in a sense that it minimizes the risk of the variable selection under 0-1 loss function. Based upon two examples, the suggested method is illustrated and compared with existing frequentist methods.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.5
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• pp.326-332
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• 2003

This paper deals with a model reduction for linear systems with time varying delayed states. A generalized controllability and observability gramians are defined and obtained by solving linear matrix inequalities. Using the generalized controllability and observability gramians, the balanced state space equation is realized. The reduced model can be obtained by truncating states in the balanced realization and the upper bound of model approximation error is also presented. In order to demonstrate efficacy of the suggested method, a numerical example is performed.

• Communications for Statistical Applications and Methods
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• v.30 no.3
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• pp.259-272
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• 2023

In this paper, we suggest a new method for the prediction of sharp changes in particulate matter (PM₁₀) using quantile mapping. To predict the current PM₁₀ density in Seoul, we consider PM₁₀ and precipitation in Baengnyeong and Ganghwa monitoring stations observed a few hours before. For the PM₁₀ distribution estimation, we use the extreme value mixture model, which is a combination of conventional probability distributions and the generalized Pareto distribution. Furthermore, we also consider a quantile generalized additive model (QGAM) for the relationship modeling between precipitation and PM₁₀. To prove the validity of our proposed model, we conducted a simulation study and showed that the proposed method gives lower mean absolute differences. Real data analysis shows that the proposed method could give a more accurate prediction when there are sharp changes in PM₁₀ in Seoul.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.494-497
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• 2002

In this paper, we have presented the simulation results about threshold voltage of nano scale lightly doped drain (LDD) MOSFET with halo doping profile. Device size is scaled down from 100nm to 40nm using generalized scaling. We have investigated the threshold voltage for constant field scaling and constant voltage scaling using the Van Dort Quantum Correction Model(QM) and direct tunneling current for each gate oxide thickness. We know that threshold voltage is decreasing in the constant field scaling and increasing in the constant voltage scaling when gate length is reducing, and direct tunneling current is increasing when gate oxide thickness is reducing. To minimize the roll-off characteristics for threshold voltage of MOSFET with decreasing channel length, we know u value must be nearly 1 in the generalized scaling.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.613-618
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• 2011

The proposed method is based on a penalized log partial likelihood of Cox proportional hazard model with L1-penalty. We use the iteratively reweighted least squares procedure to solve L1 penalized log partial likelihood function of Cox proportional hazard model. It provide the ecient computation including variable selection and leads to the generalized cross validation function for the model selection. Experimental results are then presented to indicate the performance of the proposed procedure.

• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.761-770
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• 2012

We frequently encounter outcomes of count that have extra variation. This paper considers several alternative models for overdispersed count responses such as a quasi-Poisson model, zero-inflated Poisson model and a negative binomial model with a special focus on a generalized linear mixed model. We also explain various goodness-of-fit criteria by discussing their appropriateness of applicability and cautions on misuses according to the patterns of response categories. The overdispersion models for counts data have been explained through two examples with different response patterns.