• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.349-360
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• 2006

PROC MIXED in SAS can be utilized to make inferences on parameters in a mixed model by use of Restricted Maximum Likelihood Estimation Method or Maximum Likelihood Estimation Method which has more merits than ANOVA method. A regression model with unbalanced nested error structure that belongs to a mixed model is used to construct confidence intervals on variances among groups, within groups, and regression coefficients in the model. PROC MIXED is applied to three different sample sizes for simulation. As a result of the simulation study, PROC MIXED generates confidence intervals on parameters that maintain the stated confidence coefficient in a large sample size. However, it does not generate confidence intervals that maintain the stated confidence coefficient for variance components among groups and intercept in a small sample size.

• Journal of Environmental Science International
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• v.6 no.5
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• pp.425-435
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• 1997

One-dimensional thermodynamic mixed layer model to stimulate variations of meteorological variables wish the planetary boundary layer has been developed In this study. This model consists of 2 prognostic equations, which can predict the variations of potential temperature and mixing ratio and several diagnostic equations. Physics within the surface and mixed layers has been considered seperately in the model. For the variations of the model, Its result has been analysed and compared with observated data over Ole Dukyang Bay for one day, July 23, 1992. The simulated height of mixed layer is comparable to the observation and the variations of temperature and mixing ratio in the mixed layer are also reasonably simulated. Those Imply that the model responds appropriately with given boundary conditions In sprite of Its simplilfied assumptions applied to the model and insufficient boundary and Initial conditions.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.6
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• pp.602-608
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• 2014

Mixed-model production lines are often used in manufacturing systems. In production lines, different product types are simultaneously manufactured by processing small batches. This paper describes a new welding assembly technology involving the development of experimental models for a mixed-model production line in an automobile company. Due to the extensive number of models, the design of a welding assembly system is complicated. Performance evaluation is an important phase in the design of welding assembly lines in a mixed-model production environment. In this study, a new welding assembly technology for a mixed-model production method was used to weld the package tray and dash panel of a vehicle.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.353-360
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• 2015

Various statistical models have been proposed over the last decade for spatially correlated Gaussian outcomes. The spatial linear mixed model (SLMM), which incorporates a spatial effect as a random component to the linear model, is the one of the most widely used approaches in various application contexts. Employing link functions, SLMM can be naturally extended to spatial generalized linear mixed model for non-Gaussian outcomes (SGLMM). We review popular SGLMMs on non-Gaussian spatial outcomes and demonstrate their applications with available public data.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.156-157
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• 2022

The purpose of this study is to propose a 40MPa mortar mixed design model that applies the neural network theory to minimize wasted effort in trial and error. A mixed design model was applied to each of the 60 data using fly ash, blast furnace slag fine powder and thickened rice husk powder. And in the neural network model, the optimized connection weight was obtained by repeatedly applying it to the MATLAB. The completed mixed design model was demonstrated by analyzing and comparing the predicted values of the mixed design model with those measured in the actual compressive strength test. As a result of the mixed design verification experiment, the error rates of the double mixed non-cement mortar using blast furnace slag fine powder and rice husk powder at a height of 40MPa were 3.24% and 3.4%. Mixed with fly ash and rice husk powder had an error rate of 3.94% and 5.8%. The error rate of the triple mixed non-cement mortar of the rice husk powder, fly ash, and blast furnace slag fine powder was 2.5% and 5.1%.

• IE interfaces
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• v.9 no.2
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• pp.39-45
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• 1996

This paper investigates the problems of grouping N products on an assembly line with an objective of maximizing the option grouping rate. Before developing a mixed model grouping algorithm, simulation studies are committed for developing operating rules and evaluating the layout production systems. A mixed model grouping algorithm is suggested and it is applied to the color selection lane in automobile production system, which reveals a high mixed model grouping rate.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.379-386
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• 2004

This paper deals with a mixed logit model for vaccination data. The effect of a newly developed vaccine for a certain chicken disease can be evaluated by a noninfection rate after injecting chicken with the disease vaccine. But there are a lot of factors that might affect the noninfecton rate. Some of these are fixed and others are random. Random factors are sometimes coming from the sampling scheme for choosing experimental units. This paper suggests a mixed model when some fixed factors need to have different experimental sizes by an experimental design and illustrates how to estimate parameters in a suggested model.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.145-151
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• 2004

PROC MIXED fits a variety of mixed models to data and enables one to use these fitted models to make statistical inferences about the data. However, the simulation study in this article shows that PROC MIXED using REML estimators provides one with a confidence interval, that does not keep the stated confidence coefficients, on sums of two variance components in the simple regression model with unbalanced nested error structure which is a mixed model.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.10
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• pp.1255-1263
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• 2004

An a priori test has been conducted for the dynamic mixed model which was generalized for the prediction of passive scalar field in a turbulent channel flow The results from a priori tests indicated that dynamic mixed model is capable of predicting both subgrid-scale heat flux and dissipation rather accurately. The success is attributed to the explicitly calculated resolved term incorporated into the model. The actual test of the model in a LES a posteriori showed that dynamic mixed model is superior to the widely used dynamic Smagorinsky model in the prediction of temperature statistics.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.221-230
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• 2015

Traditionally, sib-pair linkage analysis with repeated measures has employed linear mixed models, but it suffers from the lack of power to find genetic marker loci associated with a phenotype of interest. In this paper, we use a gamma mixed model to improve sib-pair linkage analysis and compare it with a linear mixed model in terms of power and Type I error. We illustrate that the use of gamma mixed model can achieve higher power than linear mixed model with Genetic Analysis Workshop 13 data.