• Journal of the Korean Data and Information Science Society
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• 제21권2호
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• pp.363-369
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• 2010

In this paper we propose a mixed-effects least squares support vector regression (LS-SVR) for longitudinal data. We add a random-effect term in the optimization function of LS-SVR to take random effects into LS-SVR for analyzing longitudinal data. We also present the model selection method that employs generalized cross validation function for choosing the hyper-parameters which affect the performance of the mixed-effects LS-SVR. A simulated example is provided to indicate the usefulness of mixed-effect method for analyzing longitudinal data.

• Communications for Statistical Applications and Methods
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• 제15권6호
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• pp.969-976
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• 2008

Mixed linear models have been widely used in various correlated data including multivariate survival data. In this paper we extend hierarchical-likelihood(h-likelihood) approach for mixed linear models with right censored data to that for left censored data. We also allow a general random-effect structure and propose the estimation procedure. The proposed method is illustrated using a numerical data set and is also compared with marginal likelihood method.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.123-130
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• 2006

This paper discusses about how to build up a mixed-effects model using cumulative logits when some factors are fixed and others are random. Location effects are considered as random effects by choosing them randomly from a population of locations. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.1215-1218
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• 2009

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of generalized regression neural networks model (GRNNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training and test performances, respectively. The training data consist of the mixed data The mixed data involves the historic data and the generated data using PARMA (1,1). And, the testing data consist of the only historic data, respectively. From this research, we evaluate the impact of GRNNM and MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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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.

• 대한기계학회논문집A
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• 제24권3호
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• pp.735-742
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• 2000

Densification behaviors of mixed metal powder under high temperature were investigated. Experimental data of mixed copper and tool steel powder with various volume fractions of Cu powder were obtained under hot isostatic pressing and hot pressing. By mixing the creep potentials of McMeeking and co-workers and of Abouaf and co-workers originally for pure powder, the mixed creep potentials with various volume fractions of Cu powder were employed in the constitutive models. The constitutive equations were implemented into a finite element program (ABAQUS) to compare with experimental data for densification of mixed powder under hot isostatic pressing and hot pressing. Finite element calculations by using the creep potentials of Abouaf and co-workers agreed reasonably well with experimental data, however, those by McMeeking and co-workers underestimate experimental data as observed in the case of pure metal powders.

• 응용통계연구
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• 제17권1호
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• pp.145-151
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• 2004

SAS의 PROC MIXED procedure는 다양한 형태의 혼합모형에 적합한 자료를 분석하고, 그 자료들이 채집된 모집단의 모수들에 관한 통계적 추론을 하는데 사용된다. 그러나 혼합모형에 해당되는 불균형중첩오차구조를 갖는 선형회귀모형안에 나타나는 두개의 분산의 합에 대한 신뢰구간을 구할 때 PROC MIXED의 REML추정량으로부터 계산되는 신뢰구간은 신뢰계수를 지키지 못한다는 것을 시뮬레이션을 통하여 보인다.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.187-193
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• 2006

This paper shows how to use continuation-ratio logits for the analysis of structured polytomous data. Here, response categories are considered to have a nested binary structure. Thus, conditionally nested binary random variables can be defined in each step. Two types of factors are considered as independent variables affecting response probabilities. For the purpose of analyzing categorical data with binary nested strutures a continuation-ratio mixed model is suggested. Estimation procedure for the unknown parameters in a suggested model is also discussed in detail by an example.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.339-345
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• 2004

This paper deals with a mixed logit model for ordered polytomous data. There are two types of factors affecting the response varable in this paper. One is a fixed factor with finite quantitative levels and the other is a random factor coming from an experimental structure such as a randomized complete block design. It is discussed how to set up the model for analyzing ordered polytomous data and illustrated how to estimate the paramers in the given model.

• Journal of the Korean Data and Information Science Society
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• 제19권2호
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• pp.413-420
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• 2008

This paper suggests a marginal logit mixed-effects for analyzing repeated binary response data. Since binary repeated measures are obtained over time from each subject, observations will have a certain covariance structure among them. As a plausible covariance structure, 1st order auto-regressive correlation structure is assumed for analyzing data. Generalized estimating equations(GEE) method is used for estimating fixed effects in the model.