• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.197-211
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• 2018

We conducted this research to examine how well regional housing prices are suited to use as an alternative to conventional census-based regional deprivation indices in health and medical geography studies. To examine the relative performance of mean regional housing prices compared to conventional census-based regional deprivation indices, we compared several multilevel logistic regression models, where the first level was individuals and the second was health districts in the Seoul Metropolitan Area (SMA) in Korea, for the sake of adjusting the regional clustering tendency of unknown factors. In these models, we predicted two dichotomous variables that represented individuals' after-lunch tooth brushing behavior and use of dental floss by individual characteristics and regional indices. Then, we compared the relative predictive performance of the models using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The results from the estimations showed that mean regional housing prices and census-based deprivation indices were correlated with the two types of dental health behavior in a statistical sense. The results also revealed that the model with mean regional housing prices showed smaller AIC and BIC compared with other models with conventional census-based deprivation indices. These results imply that it is possible for housing prices summarized using aerial units to be used as an alternative to conventional census-based deprivation indices when the census variables employed cannot properly reflect the characteristics of the aerial units.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.155-159
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• 2004

Recently, there are many trials about Artificial neural networks : ANNs structure and studying method of researches for forecasting traffic volume. ANNs have a powerful capabilities of recognizing pattern with a flexible non-linear model. However, ANNs have some overfitting problems in dealing with a lot of parameters because of its non-linear problems. This research deals with the application of a variety of model selection criterion for cancellation of the overfitting problems. Especially, this aims at analyzing which the selecting model cancels the overfitting problems and guarantees the transferability from time measure. Results in this study are as follow. First, the model which is selecting in sample does not guarantees the best capabilities of out-of-sample. So to speak, the best model in sample is no relationship with the capabilities of out-of-sample like many existing researches. Second, in stability of model selecting criterion, AIC3, AICC, BIC are available but AIC4 has a large variation comparing with the best model. In time-series analysis and forecasting, we need more quantitable data analysis and another time-series analysis because uncertainty of a model can have an effect on correlation between in-sample and out-of-sample.

• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.111-116
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• 2015

The purpose of this study was to evaluate mixed model of dbh-height relation containing random effect. Data were obtained from a survey site for Quercus variabilis in Gangwon region and remeasured the same site after three years. The mixed model were used to fixed effect in the dbh-height relation for Quercus variabilis, with random effect representing correlation of survey period were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -0.0291, 0.1007, respectively. The model with random effect (AIC = -215.5) has low AIC value, comparison with model with fixed effect (AIC = -154.4). It is for this reason that random effect associated with categorical data is used in the data fitting process, the model can be calibrated to fit repeated site by obtaining measurements. Therefore, the results of this study could be useful method for developing model using repeated measurement.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1169-1178
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• 2010

This article consider a performance model selection based on AIC under unbalanced deign in linear mixed effect models. Vaida and Balanchard (2005) proposed conditional AIC for model selection in linear mixed effect models when the prediction of random effects is of primary interest. Theoretical properties of cAIC and related criteria have been investigated by Liang et al. (2008) and Greven and Kneib (2010). However, all of the simulation studies were performed under a balanced design. Even though functional form of AIC remain same even under the unbalanced deign, it is worthwhile to investigate performance of AIC based model selection criteria under the unbalanced design. The simulation study in this article shows how unbalancedness affects model selection in linear mixed effect models.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.12
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• pp.1605-1612
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• 2012

The distribution of a response usually depends on the distribution of the variables. When a variable shows a distribution with two different modes, the response also shows a distribution with two different modes. In this case, recently developed methods for reliability analysis assume that the distribution functions are continuous with a mode. In actual problems, however, because information is often provided in a discrete form with two or more modes, it is important to estimate the distributions for such information. In this study, we employ the finite mixture model to estimate the response distribution with two different modes, and we select the best candidate distribution through AIC. Mathematical examples are illustrated to verify the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.7
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• pp.883-889
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• 2013

The distribution of a response usually depends on the distribution of a variable. When the distribution of a variable has two different modes, the response also follows a distribution with two different modes. In most reliability analysis methods, the number of modes is irrelevant, but not the type of distribution. However, in actual problems, because information is often provided with two or more modes, it is important to estimate the distributions with two or more modes. Recently, some reliability analysis methods have been suggested for bimodal distributions. In this paper, we review some methods such as the Akaike information criterion (AIC) and maximum entropy principle (MEP) and compare them with the Monte Carlo simulation (MCS) using mathematical examples with two different modes.

• Journal of Ecology and Environment
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• v.32 no.2
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• pp.103-107
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• 2009

We investigated colonization and extinction patterns in a meta population of the gold-spotted pond frog (Rana planeyi ehoseniea) near the Korea National University of Education, Chungbuk, Korea, by surveying the frogs in the nine occupied habitat patches in the study area four times per breeding season for three years (2006$\sim$2008) and recording whether the patches were occupied by frogs as well as how many frogs were calling in the patches. We then developed five a priori year-specific models using the Akaike Information Criterion (AIC). The models predicted that: 1) probabilities of colonization and local extinction of the frogs were better explained by year-dependent models than by constant models, 2) there are high local extinction and low colonization probabilities, 3) approximately 31% number of patches will be occupied at equilibrium, and 4) that considerable variation in occupation rate should occur over a 30-year period, due to demographic stochasticity (in our model, the occupation rate ranged from 0.222 to 0.889). Our results suggest that colonization is important in this metapopulation system, which is governed by mainly stochastic components, and that more constructive conservation effects are needed to increase local colonization rates.

• Journal of Wetlands Research
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• v.20 no.4
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• pp.338-344
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• 2018

In this study, the surface air temperature (SAT) and the dew-point temperature (DPT) are applied as the covariance of the location parameter among three parameters of GEV distribution to reflect the non-stationarity of extreme rainfall due to climate change. Busan station is selected as the study site and the monthly maximum daily rainfall depth from May to October is used for analysis. Various models are constructed to select the most appropriate co-variate(SAT and DPT) function for location parameter of GEV distribution, and the model with the smallest AIC(Akaike Information Criterion) is selected as the optimal model. As a result, it is found that the non-stationary GEV distribution with co-variate of exp(DPT) is the best. The selected model is used to analyze the effect of climate change scenarios on extreme rainfall quantile. It is confirmed that the design rainfall depth is highly likely to increase as the future DPT increases.

• Journal of Forest and Environmental Science
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• v.34 no.6
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• pp.451-456
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• 2018

The use of single component distribution to describe the irregular stand structure of degraded forest often lead to bias. Such biasness can be overcome by the application of finite mixture distribution. Therefore, in this study, finite mixture distribution was used to characterise the irregular stand structure of the Gmelina arborea plantation in Omo forest reserve. Thirty plots, ten each from the three stands established in 1984, 1990 and 2005 were used. The data were pooled per stand and fitted. Four finite mixture distributions including normal mixture, lognormal mixture, gamma mixture and Weibull mixture were considered. The method of maximum likelihood was used to fit the finite mixture distributions to the data. Model assessment was based on negative loglikelihood value ($-{\Lambda}{\Lambda}$), Akaike information criterion (AIC), Bayesian information criterion (BIC) and root mean square error (RMSE). The results showed that the mixture distributions provide accurate and precise characterisation of the irregular diameter distribution of the degraded Gmelina arborea stands. The $-{\Lambda}{\Lambda}$, AIC, BIC and RMSE values ranged from -715.233 to -348.375, 703.926 to 1433.588, 718.598 to 1451.334 and 3.003 to 7.492, respectively. Their performances were relatively the same. This approach can be used to describe other irregular forest stand structures, especially the multi-species forest.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.8
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• pp.868-874
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• 1990

A singular value approach is presented in order to determine the number of multiple sinusoids from the finite noisy data. Simulations are conducted for Akaike's information criterion (AIC), Rissanen's shortest data description (MDL) and a singular value approach, for various examples with different SNR's and methods of estimating frequencies. And then the performances are compared. Simulation results that the singular value approach is superior to AIC and MDL for FBLP, HOYW and covariance matrix based methods are investigated. The approach with contribute to the frequency estimation of multiple sinusoids from the finite noisy data. Furthermore, this will be applied to the DSPs of communication and bio-medical engineering.