• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.627-641
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• 2016

In this paper, we consider a seemingly unrelated regression (SUR) model and propose a nonparametric Bayesian approach to SUR with a Dirichlet process mixture of normals for modeling an unknown error distribution. Posterior distributions are derived based on the proposed model, and the posterior inference is performed via Markov chain Monte Carlo methods based on the collapsed Gibbs sampler of a Dirichlet process mixture model. We present a simulation study to assess the performance of the model. We also apply the model to precipitation data over South Korea.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.205-213
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• 2007

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.

• Journal of Korean Society for Quality Management
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• v.34 no.3
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• pp.62-65
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• 2006

We can use the ridge regression as a means for stabilizing the coefficient estimators in the fitted model when performing experiments in highly constrained regions causes collinearity problems in mixture experiments. But there is no theory available on which to base statistical inference of ridge estimators. The bootstrap could be used to seek the confidence intervals of ridge estimators.

• Archives of Pharmacal Research
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• v.11 no.1
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• pp.45-51
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• 1988

The feature of resolution enhancement by derivative operation is linked to one of the multivariate analysis, which is multiple linear regression with two options, all possible and stepwise regression. Examined samples were synthetic mixtures of 5 vitamins, thiamine mononitrate, riboflavin phosphate, nicotinamide, pyridoxine hydrochloride and ascorbic acid. All components in mixture were quantified with reasonably good accuracy and precision. Whole data processing procedure was accomplished on-line by the development of three computer programs written in APPLESOFT BASIC language.

• Fire Science and Engineering
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• v.18 no.1
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• pp.1-6
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• 2004

In the reutilization process using limonene, the organic solvent to reduce volume of EPS, the AIT was measured with the variation of concentration and volume of mixture, in order to present the fund-mental data on the fire hazard assessment of limonene - EPS mixture at storage and handling. And ignition zone was compared with non-ignition zone. The equation related to AIT, activation energy and ignition delay time, used by the most scientific basis for predicting AIT values, was suggested using linear regression analysis as ln t = 0.704/T-5.819. And the equation related to concentration of mixture and AIT was also suggested to predict ignition hazard of combustible mixture using nonlinear regression analysis as $T_m/=248.32+69.27X+172.60X^2$. It enabled to predict ignition temperature according to variation of ignition delay time and concentration of mixture by the suggested equations.

• 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.

• Journal of Environmental Science International
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• v.24 no.11
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• pp.1541-1546
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• 2015

The objective of this research was to evaluate the toxicity of the industial xenobiotic Aroclor 1248 (A) and natural origin substances~elemental sulfur (S80) and oleic acid (OA) and their binary mixtures to V. fischeri bioluminescence during the prolonged exposure time (up to 60 min). The bioluminescence quenching test was used to determine the toxic effects. Full factorial experiment design and multiple regression analysis and the comparison of binary mixture effect with the sum of effects of individual chemicals were used for the evaluation of combined effects of toxicants. The analysis of general trend of mixture toxicity to bioluminescence showed that mixture toxic effects were reversible up to 60 min. Data analysis revealed different joint effects, which were depended on mixture composition. S80 enhanced toxic effect of A and acted additively with synergistic interaction. Hydrophobic OA in mixture with A acted antagonistically and in mixture with sulfur caused an additive effect with antagonistic component of interaction. It was concluded that low concentrations of natural toxic substances present in environmental samples as mixtures of chemicals can define the toxicodynamic character of industrial xenobiotics.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.759-768
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• 2011

Tree algorithms have been widely developed for regression problems. One of the good features of a regression tree is the flexibility of fitting because it can correctly capture the nonlinearity of data well. Especially, data with sudden structural breaks such as the price of oil and exchange rates could be fitted well with a simple mixture of a few piecewise linear regression models. Now that split points are determined by chi-squared statistics related with residuals from fitting piecewise linear models and the split variable is chosen by an objective criterion, we can get a quite reasonable fitting result which goes in line with the visual interpretation of data. The piecewise linear regression by a regression tree can be used as a good fitting method, and can be applied to a dataset with much fluctuation.

• Journal of Korean Society for Quality Management
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• v.39 no.3
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• pp.400-411
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• 2011

Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components-process variables experiments depend on the mixture components-process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. In this paper we propose three starting models for the mixture components-process variables experiments. One of the starting model we are considering is the model which includes product terms up to cubic order interactions between mixture effects and the linear & pure quadratic effect of the process variables from the product model. In this paper, we propose a method for finding robust designs and practical designs with respect to D-, G-, and I-optimality for the various starting combined models and then, we find practically efficient and robust designs for estimating the regression coefficients for those models. We find the prediction capability of those recommended designs in the case of three components and three process variables to be good by checking FDS(Fraction of Design Space) plots.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.10a
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• pp.301-308
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• 1999

With the shortage of the land and NIMBY syndrome, it is issued recently that the capacity of waste-landfill site is needed though the decreasing tendency of waste landfill. From this point, the stability is the most essential problem in the landfill that will be constructed. Advanced design and construction are most important for that. In this paper, for the study of desiccation, dry-shrinkage crack from drying and chemical reaction in cement hydration, which is occurred when the surface layer stabilization method is applied in wast landfill, laboratory test of the ground and specimen according to the mixture ratio of stabilizer is performed. From the result, it is notified that the uni-axial strength increases with the stabilizer, but dry-shrinkage increases too, therefore, it is important and the goal of this study to find the optimal mixture ratio of each stabilizer. Analysis of variance for regression with acting variables is performed to find optimal mixture ratio of each stabilizer.