• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.69-74
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• 2003

The estimation of pest density is a prime concern of Integrated Pest Management (IPM) because the success of artificial intervention such as spraying pestcides or natural enemies depends on pest density. Also, the spatial pattern of pest population within plants or plots has been studies in various ways. In this study, we applied generalized linear mixed model to Tetranychus urticae Koch , two-spotted spider mite count in glasshouse grown roses. For this analysis, the subject-specific as well as pupulation-averaged approaches are used.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.795-805
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• 1997

In this paper, we consider a Bayesian forecasting method for the analysis of repeated surveys. It is assumed that the parameters of the superpopulation model at each time follow a stochastic model. We propose Bayesian prediction procedures for the finite population total under dynamic generalized linear models. Some numerical studies are provided to illustrate the behavior of the proposed predictors.

• Journal of the Korean Statistical Society
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• v.33 no.1
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• pp.59-78
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• 2004

A regression function in generalized linear model may have a discontinuity/change point at unknown location. In order to estimate the location of the discontinuity point and its jump size, the strategy is to use a nonparametric approach based on one-sided kernel weighted local-likelihood functions. Weak convergences of the proposed estimators are established. The finite-sample performances of the proposed estimators with practical aspects are illustrated by simulated examples.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.27-41
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• 2008

A graphical method of checking the adequacy of a generalized linear model is proposed. The graph helps to assess the assumption that the link function of mean can be expressed as a linear combination of explanatory variables in the generalized linear model. For the graph the boosting technique is applied to estimate nonparametrically the relationship between the link function of the mean and the explanatory variables, though any other nonparametric regression methods can be applied. Through simulation studies with normal and binary data, the effectiveness of the graph is demonstrated. And we list some limitations and technical details of the graph.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.697-699
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• 2000

In this paper, an extension of model predictive controller for nonlinear process using Takagi-Sugeno(TS) fuzzy model is proposed Since the consequent parts of TS fuzzy model comprise linear equations of input and output variables. it is locally linear, and the Generalized Predictive Control(GPC) technique which has been developed to control Linear Time Invariant(LTI) plants, can be extended as a parallel distributed controller. Also fuzzy soft constraints are introduced to handle both equality and inequality constraints in a unified form. So the traditional constrained GPC can be transferred to a standard fuzzy optimization problem. The proposed method conciliates the advantages of the fuzzy modeling with the advantages of the constrained predictive control, and the degree of freedom is increased in specifying the desired process behavior.

• Korean Journal of Social Welfare
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• v.58 no.2
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• pp.119-141
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• 2006

This study aims to explore the status and characteristics of the working poor and to identify the major determinants of their statistic status. For this, longitudinal panel data (from 2nd wave(1999) data to 7th wave(2004) data) from Korean Labor and Income Panel Study (KLIPS), is used. The data is analyzed by adopting Hierarchical Generalized Linear Model (HGLM), which is known as an app.opriate data analysis method for the hierarchically structured data, to look at the factors that affect on the poverty status of the working people. The results show that 1) it is estimated that about 1 out of 10 working people (about 10.0%) are poor, and 2) sex, education level, marital status, region where they lives, employment status, occupation type, and industry type that they are working at are significant predictors in determining their poverty status. Unlike the results of the previous studies, however, the number of the household member, age are not influenced on their poverty status. Based on these results, several policy implications are presented at the end of this paper.

• 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.

• Journal of the military operations research society of Korea
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• v.7 no.2
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• pp.63-99
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• 1981

The generalized goal decomposition model proposed by Ruefli as a single period decision model is presented for the purpose of a review and extended to make a multiple period planning model. The multiple period planning model in the three level organization is formulated with, linear goal deviations by introducing the goal programming method. Dynamic formulation using the generalized goal decomposition model for each single period problem is also presented. An iterative search algorithm is presented as an appropriate solution method of the dynamic formulation of the multiple period planning model.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.807-815
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• 2002

We consider a more general linear regression super-population model than the one of Chaudhuri and Stronger(1992) . We can find the same type of the best linear unbiased(BLU) predictor as that of Chaudhuri and Stenger and see that the optimal design is again a purposive one which prescribes choosing one of the samples of size n which has $\chi$ closest to $\bar{X}$.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.209-216
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• 2015

Quantile regression models with covariate measurement errors have received a great deal of attention in both the theoretical and the applied statistical literature. A lot of effort has been devoted to develop effective estimation methods for such quantile regression models. In this paper we propose the partially linear support vector orthogonal quantile regression model in the presence of covariate measurement errors. We also provide a generalized approximate cross-validation method for choosing the hyperparameters and the ratios of the error variances which affect the performance of the proposed model. The proposed model is evaluated through simulations.