• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.507-518
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• 2015

It is not easy to select a linear mixed model since the main interest for model building could be different and the number of parameters in the model could not be clearly defined. In this paper, performance of conditional Akaike Information Criteria and its bias-corrected version are compared with marginal Bayesian and Akaike Information Criteria through a simulation study. The results from the simulation study indicate that bias-corrected conditional Akaike Information Criteria shows promising performance when candidate models exclude large models containing the true model, but bias-corrected one prefers over-parametrized models more intensively when a set of candidate models increases. Marginal Bayesian and Akaike Information Criteria also have some difficulty to select the true model when the design for random effects is nested.

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.435-445
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• 2007

This paper addresses for the first time the use of complete data information criteria in unsupervised learning of the Naive-Bayes model. A Monte Carlo study sets a large experimental design to assess these criteria, unusual in the Bayesian network literature. The simulation results show that complete data information criteria underperforms the Bayesian information criterion (BIC) for these Bayesian networks.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.205-214
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• 1996

As flexible Bayesian test criteria for nested point null hypotheses of multiple regression coefficients, partial and overall Bayes factors are introduced under a class of intuitively meaningful prior. The criteria lead to a simple method for considering different prior beliefs on the subspaces that constitute a partition of the coefficient parameter space. A couple of tests are suggested based on the criteria. It is shown that they enable us to obtain pairwise comparisons of hypotheses of the partitioned subspaces. Through a Monte Carlo simulation, performance of the tests based on the criteria are compared with the usual Bayesian test (based on Bayes factor)in terms of their respective powers.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.11-19
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• 1997

One of the most important issues in regression is variable selection problem. Recently several methods have been proposed to overcome the overparameterization property of Mallow's $C_p$. In this paper we compare these model selection criteria in view of the performance of selecting true model by simulation study.

• Journal of Advanced Engineering and Technology
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• v.11 no.4
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• pp.287-294
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• 2018

Since its introduction in Korea, design VE has widely been used as a means to enhance values in the construction industry. However, a greater emphasis is still placed on cost reduction in approach attitudes and performance evaluations on the implementation of design VE. In this regard, this study presented a performance evaluation method for cost, function, and value of VE proposals. Numerical criteria on the increase and decrease of cost and function that can classify the value enhancement type of VE proposals were proposed based on the performance evaluation method. It is expected that the use of numerical criteria for the type classification of VE proposal, and cost and performance evaluation method proposed in this study will make it possible to conduct a clear and more intuitive evaluation of VE proposal. However, it is appropriate to use the numerical criteria as a guideline to apply the new performance evaluation method for VE proposals. Therefore, it is necessary to conduct a statistical analysis with a wider range of users after the repeated application of the findings of this study, and thus to carry out research for presenting the numerical criteria for various types of users.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.335-345
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• 2000

Most context use the classical norms on function spaces as the error criteria. Since these norms are all based on the vertical distances between the curves, these can be quite inappropriate from a visual notion of distance. Visual errors in Marron and Tsybakov(1995) correspond more closely to "what the eye sees". Simulation is performed to compare the performance of the regression smoothers in view of MISE and the visual error. It shows that the visual error can be used as a possible candidate of error criteria in the kernel regression estimation.

• Journal of Environmental Science International
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• v.12 no.7
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• pp.809-816
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• 2003

The short-term version of Industrial Source Complex Model(ISCST3) was evaluated for estimating short-term concentrations using criteria pollutant(SO$_2$, NO$_2$, CO, PM10) data from emission inventory of Young Tong area in Suwon for the year 2002. The contribution of pollutant concentration from point, line, area sources was found 21.8, 76.5 and 1.6%. Statistical parameters, such as correlation coefficient, index of agreement(IA), normalized mean square error(NMSE) and fractional bias(FB) were calculated for each pollutants. The model performance were found good for PM10(82%) and NO$_2$(69%), but poor for SO$_2$(34%) and CO(13%).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.89-90
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• 2011

• Atmosphere
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• v.30 no.3
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• pp.257-276
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• 2020

Faithful evaluation of the meteorological input is a prerequisite for a better understanding of air quality model performance. Despite the importance, the preliminary meteorological assessment has rarely been concerned. In this study, we aim to evaluate the performance of the Weather Research and Forecasting (WRF) model conducting a year-long high-resolution meteorological simulation in 2016 over the Seoul metropolitan area. The WRF model was configured based on a series of sensitivity simulations of initial/boundary meteorological conditions, land use mapping data, reanalysis grid nudging method, domain nesting method, and urban canopy model. The simulated results of winds, air temperature, and specific humidity in the atmospheric boundary layer (ABL) were evaluated following statistical evaluation guidance using the surface and upper meteorological measurements. The statistical evaluation results are presented. The model performance was interpreted acceptable for air quality modeling within the statistical criteria of complex conditions, showing consistent overestimation in wind speeds. Further statistical analysis showed that the meteorological model biases were highly systematic with systematic bias fractions (f_SB) of 20~50%. This study suggests that both the momentum exchange process of the surface layer and the ABL entrainment process should be investigated for further improvement of the model performance.

• Journal of the Korea Management Engineers Society
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• v.23 no.4
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• pp.73-86
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• 2018

Most electric products produced during the manufacturing process are produced after design and mass production under a given control standard. In particular, the development phase should present the criteria for the production process by setting appropriate limits based on the performance being targeted. Even if the standard of performance is set considering the performance of the process, measuring the performance of the product after actual production results will cause nonconformities with the expected results. Among the performance of electrical products, Energy standards represented by energy consumption efficiency continue to be of importance, and are mandatory standards that correspond to national standards in most countries. Therefore, statistical quality control of these standards shall basically have a large number of test equipment for each product, ensure sufficient test time and continuous sampling of product samples. In the end, companies that produce and sell electric appliances are striving to control mass production at a great cost, but this is not acceptable. This study presents basic characteristics of the energy efficiency of electrical products and proposes and conducts a case study on statistical production control methods for performance variation across products under the standards about domestic and international regulations.