• 대한인간공학회지
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• 제22권4호
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• pp.59-78
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• 2003

Most regression models of anthropometric variables use stature and/or weight as regressors; however, these 'flat' regression models result in large errors for anthropometric variables having low correlations with the regressors. To develop more accurate regression models for anthropometric variables, this study proposed a method to estimate anthropometric variables in a hierarchical manner based on the relationships among the variables and a process to develop and improve corresponding regression models. By applying the proposed approach, a hierarchical estimation structure was constructed for 59 anthropometric variables selected for the occupant package design of a passenger car and corresponding regression models were developed with the 1988 US Army anthropometric survey data. The hierarchical regression models were compared with the corresponding flat regression models in terms of accuracy. As results, the standard errors of the hierarchical regression models decreased by 28% (4.3mm) on average compared with those of the flat models.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.227-234
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• 2002

In this paper, we consider hierarchical Bayes generalized linear models for the analysis of longitudinal count data. Specifically we introduce the hierarchical Bayes random effects models. We discuss implementation of the Bayes procedures via Markov chain Monte Carlo (MCMC) integration techniques. The hierarchical Baye method is illustrated with a real dataset and is compared with other statistical methods.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.115-128
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• 2002

Hierarchical models are widely used for inference on correlated parameters as a compromise between underfitting and overfilling problems. In this paper, we take a Bayesian approach to analyzing hierarchical models and suggest a Markov chain Monte Carlo methods to get around computational difficulties in Bayesian analysis of the hierarchical models. We apply the method to a real data on smoking and lung cancer which are collected from cities in China.

• Journal of the Korean Data and Information Science Society
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• 제20권5호
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• pp.895-903
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• 2009

The penalized partial likelihood based on restricted maximum likelihood method has been widely used for the inference of frailty models. However, the standard-error estimate for frailty parameter estimator can be downwardly biased. In this paper we show that such underestimation can be corrected by using hierarchical likelihood. In particular, the hierarchical likelihood gives a statistically efficient procedure for various random-effect models including frailty models. The proposed method is illustrated via a numerical example and simulation study. The simulation results demonstrate that the corrected standard-error estimate largely improves such bias.

• 한국시뮬레이션학회논문지
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• 제5권2호
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• pp.25-40
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• 1996

Modeling of systems for intensive knowledge-based processing requires a modeling methodology that makes efficient access to the information in huge data base models. The proposed level access mothodology is a modeling approach applicable to systems where data is stored in a hierarchical and modular modules of active memory cells(processor/memory pairs). It significantly reduces the effort required to create discrete event simulation models constructed in hierarchical, modular fashion for above application. Level access mothodology achieves parallel access to models within the modular, hierarchical modules(clusters) by broadcasting the desired operations(e.g. querying information, storing data and so on) to all the cells below a certain desired hierarchical level. Level access methodology exploits the capabilities of object-oriented programming to provide a flexible communication paradigm that combines port-to-port coupling with name-directed massaging. Several examples are given to illustrate the utility of the methodology.

• 한국군사과학기술학회지
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• 제13권1호
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• pp.41-49
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• 2010

In this paper, we propose a hierarchical RAM simulation model framework which are used to analyze the RAM specifications on the concept refinement phase. The hierarchical RAM simulation model framework consists of RAM simulation models, class library and each model's input and output data lists. The hierarchical RAM simulation models are co-operated with 3 kinds of model - type I, II, III. Type I, II models are used to analyze the target operational availability and Type III is used to establish the initial RAM specifications. Each model's input and output data lists are defined by considering each model's purpose of RAM analysis. The class library is arranged with each model's classes for implementing the hierarchical simulation models. The proposed framework may be applied for executing the RAM activities effectively.

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.357-366
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• 1999

Let us assume that two different log-linear models are selected by various model selection methods. When these are non-hierarchical it is not easy to choose one of these models. In this paper the well-known Cox's statistic is applied to compare these non-hierarchical log-linear models. Since it is impossible to obtain the analytic solution about the problem we proposed a alternative method by extending Pesaran and pesaran's (1993) simulation approach. We find that the values of proposed test statistic and the estimates are very much stable with some empirical results.

• 응용통계연구
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• 제14권1호
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• pp.161-175
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• 2001

메타분석(Meta-analysis)은 서로 독립적으로 연구되어진 결과들을 전체적인 하나의 결과로 도출하기 위해 사용되어지는 통계적 방법이다. 이러한 통계적 방법을 설명할 모형으로는 선택모형(selection model)을 포함한 계층적 모형(hierarchical model)을 사용하며, 이러한 모형들은 베이지안 메타분석에 유용한 것으로 알려져 있다. 그러나, 메타분석의 자료들은 일반적으로 출판편의(publication bias)를 갖고 있으므로 이를 극복하고자 가중함수(weight function)를 이용하여 분포함수를 새롭게 정의하여 사용한다. 최근에 Silliman(1997)은 계층적 모형(hierarchical model)에 가중함수를 첨부한 계층적 선택모형(hierarchical selection model)을 정의하고 모수적 베이지안 방법을 제시하였다. 본 연구에서는 미관측된 연구효과에 디리슈레 과정 사전분포(Dirichlet process prior)를 적용한 준모수적 계층적 선택모형(semiparametric hierarchical selection models)을 소개한다. 여기서 제시된 준모수적 계층적 선택모형을 베이지안 방법으로 추정하기 위하여 마코프 연쇄 몬테칼로(Markov chain Monte Carlo)방법을 이용한다. 제시된 방법을 적용하기 위하여 실제 자료(Johnson, 1993)인 충치를 예방하기 위한 두 가지의 예방약의 효과에 대한 차이를 비교하기 위해 얻어진 12개의 연구를 이용하여 메타분석을 한다.

• Communications for Statistical Applications and Methods
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• 제9권2호
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• pp.315-324
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• 2002

For estimating the error variance under the relative squared error loss in two-way analysis of variance models, we provide a class of hierarchical Bayes estimators and then derive a subclass of the hierarchical Bayes estimators, each member of which dominates the best multiple of the error sum of squares which is known to be minimax. We also identify a subclass of non-minimax hierarchical Bayes estimators.

• 한국시뮬레이션학회논문지
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• 제8권4호
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• pp.89-107
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• 1999

There are many issues in computer simulation such as verifying model code, validating models, understanding the dynamics of systems and training the personnel. The developers of simulation tool have been interested in the animation since it can help solve the problems related to the above listed issues. In practice, animation is one of the popular method for displaying the simulation output for solving these problems. Trying to display all the graphic objects representing the dynamics of the models being simulated, however, causes the distraction of focus, which results in solving the above listed problems difficult. The redundant graphic objects also Increase the computer computation overhead. This paper presents a hierarchical animation environment in which the users can have better focus on the dynamics of system components. In hierarchical animation environment the users can observe the dynamics of system by selectively choosing the hierarchical level and components with in a level of the hierarchically structured model. Especially when the model is large and complex the selection of observation level is needed. The design approach of the hierarchical animator is based on the DEVS(Discrete Event system Specification) formalism which is theoretically well grounded means of expressing modular and hierarchical models.