• Genomics & Informatics
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• 제1권1호
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• pp.32-39
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• 2003

We present a latent variable model-based approach to the analysis of gene expression patterns, coupled with topographic clustering. Aspect model, a latent variable model for dyadic data, is applied to extract latent patterns underlying complex variations of gene expression levels. Then a topographic clustering is performed to find coherent groups of genes, based on the extracted latent patterns as well as individual gene expression behaviors. Applied to cell cycle­regulated genes of the yeast Saccharomyces cerevisiae, the proposed method could discover biologically meaningful patterns related with characteristic expression behavior in particular cell cycle phases. In addition, the display of the variation in the composition of these latent patterns on the cluster map provided more facilitated interpretation of the resulting cluster structure. From this, we argue that latent variable models, coupled with topographic clustering, are a promising tool for explorative analysis of gene expression data.

• Communications for Statistical Applications and Methods
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• 제11권1호
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• pp.49-58
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• 2004

In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

• Communications for Statistical Applications and Methods
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• 제24권2호
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• pp.173-191
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• 2017

This study develops a new type of latent class analysis (LCA) in order to explain the associations between one latent variable and several other categorical latent variables. Our model postulates that the prevalence of the latent variable of interest is affected by another latent variable composed of other several latent variables. For the parameter estimation, we propose deterministic annealing EM (DAEM) to deal with local maxima problem in the proposed model. We perform simulation study to demonstrate how DAEM can find the set of parameter estimates at the global maximum of the likelihood over the repeated samples. We apply the proposed LCA model in an investigation of the effect of and joint patterns for drug-using behavior to violent behavior among US high school male students using data from the Youth Risk Behavior Surveillance System 2015. Considering the age of male adolescents as a covariate influencing violent behavior, we identified three classes of violent behavior and three classes of drug-using behavior. We also discovered that the prevalence of violent behavior is affected by the type of drug used for drug-using behavior.

• 응용통계연구
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• 제28권1호
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• pp.61-74
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• 2015

최근 교육학, 경영학, 심리학 등 사회과학 뿐만 아니라 공정관리, 생물정보학 등 자연과학에서도 널리 사용되고 있는 구조식 모형(structural equation modeling)에서 잠재변수점수(latent variable score)는 직접 측정이 불가능한 잠재변수를 수량화한 추정치이다. 이 연구에서는 구조식 모형을 단계(stage)별로 분할하여 분석하는 단계별 구조식 모형(stagewise SEM; SSEM)을 제안하였다. 기존 방법은 모든 관측변수의 분산-공분산을 한꺼번에 고려하므로 독립변수인 외생잠재변수(exogenous latent variable)가 종속변수인 내생잠재변수(endogenous latent variable)에 의해 영향을 받는, 논리적으로 타당하지 않은 경우가 있다. 단계별 구조식 모형은 이런 문제점을 해결할 뿐만 아니라 모형의 복잡성을 낮추어 쉽게 해를 찾을 수 있으며, 분석과정에서 생성되는 잠재변수점수로 추가 분석도 용이하다.

• Communications for Statistical Applications and Methods
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• 제8권3호
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• pp.777-786
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• 2001

The autoregressive models have been used to describe a wade variety of time series. Then the problem of determining the order in the times series model is very important in data analysis. We consider the Bayesian approach for finding the order of autoregressive(AR) error models using the latent variable which is motivated by Tanner and Wong(1987). The latent variables are combined with the coefficient parameters and the sequential steps are proposed to set up the prior of the latent variables. Markov chain Monte Carlo method(Gibbs sampler and Metropolis-Hasting algorithm) is used in order to overcome the difficulties of Bayesian computations. Three examples including AR(3) error model are presented to illustrate our proposed methodology.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.885-897
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• 2000

The use of an unweighted mean and of separate tests is part of the current practice for analyzing interlaboratory studies, and we hope to improve on this method. We fit, using maximum likelihood(ML), a rather intricate, multi-parameter measurement model with the material's true value as a latent variable in a situation where quite serviceable regression and ANOVA calculations have already been developed. The model fit leads to both a weighted estimate of he overall mean, and to tests for equality of means, slopes and variances. Maximum likelihood tests for difference among variances poses a challenge in that the likelihood can easily becoem unbounded. Thus the major objective become to provide a useful test of variance equality.

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.219-227
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• 2001

Another approach (multi-parameter measurement method) of interlaboratory studies of test methods is presented. When the unrestricted normal likelihood for the fixed latent variable model is unbounded, we propose a me쇙 of restricting the parameter space by formulating realistic alternative hypothesis under which the likelihood is bounded. A simulation study verified the claim of conservatism of level of significance based on assumptions about central chi-square distributed test statistics and on Bonferroni approximations. We showed a randomization approach that furnished empirical significance levels would be better than a Bonferroni adjustment.

• 한국도로학회논문집
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• 제17권3호
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• pp.117-124
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• 2015

PURPOSES : The purpose of this study is to verify traffic accident injury severity factors for elderly drivers and the relative relationship of these factors. METHODS : To verify the complicated relationship among traffic accident injury severity factors, this study employed a structural equation model (SEM). To develop the SEM structure, only the severity of human injuries was considered; moreover, the observed variables were selected through confirmatory factor analysis (CFA). The number of fatalities, serious injuries, moderate injuries, and minor injuries were selected for observed variables of severity. For latent variables, the accident situation, environment, and vehicle and driver factors were respectively defined. Seven observed variables were selected among the latent variables. RESULTS : This study showed that the vehicle and driver factor was the most influential factor for accident severity among the latent factors. For the observed variable, the type of vehicle, type of accident, and status of day or night for each latent variable were the most relative observed variables for the accident severity factor. To verify the validity of the SEM, several model fitting methods, including ${\chi}^2/df$, GFI, AGFI, CFI, and others, were applied, and the model produced meaningful results. CONCLUSIONS : Based on an analysis of results of traffic accident injury severity for elderly drivers, the vehicle and driver factor was the most influential one for injury severity. Therefore, education tailored to elderly drivers is needed to improve driving behavior of elderly driver.

• 대한교통학회지
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• 제28권4호
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• pp.119-128
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• 2010

통행자의 통행수단선택행태는 개인의 사회경제적 속성과 수단의 속성뿐만 아니라 통행자 개인의 잠재된 통행행태 즉, 통행행태적 선호도 역시 큰 영향을 미친다. 통행수단선택행태에 특정한 영향을 미치는 잠재통행행태를 수단선택모형에 반영한다면 비관측요인에서 기인하는 오차를 크게 줄일 수 있고, 통행행태에 대한 다양하고 보다 합리적인 해석이 가능하다. 본 연구에서는 한강 수상대중교통 도입에 따른 수단선택행태조사 자료를 활용하여 잠재변수를 정의하고 새로운 수단에 대한 분석이 가능한 RP/SP 결합모형을 구축하였다. 잠재변수는 행태설문조사자료를 기반으로 요인분석을 통해 계량화하였다. 모형구축결과 통행자들의 잠재된 통행행태는 수단선택행태에 유의한 영향을 미치고, 잠재변수를 반영한 경우 모형의 적합도가 향상되었다. 또한 잠재행태 관점에서도 RP/SP 결합모형이 SP모형에 비해 합리적인 것으로 분석되었다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 가을 학술발표논문집 Vol.32 No.2 (2)
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• pp.262-264
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• 2005

인유두종 바이러스(Human Papillomavirus: HPV)는 사마귀로부터 생식기 및 배설기의 침윤성 암에 이르기까지 여러 질병과 연관되어 있음이 알려져 있다. 현재 200종 이상이 알려져 있고, 이 중 85개는 전체 유전자가 밝혀져 있다. HPV 감염 시 만들어지는 단백질 중 E6. E7 단백질은 암 억제 유전자(p53, pRb)에 결합하여 세포의 암 억제 기능을 저하시키고 이로 인해 암을 발생시킨다. 본 논문은 암 발생과 밀접한 관련이 있는 HPV의 E6 단백질 서열과 HPV 유형(HPV Type)을 가지고, PLSA (Probabilistic Latent Semantic Analysis) 방법을 이용하여 HPV를 클러스터링(clustering) 해 보았다. 실험 결과, 특정 클러스터는 질병과 밀접하게 연관되어 있으며, 이와 관련된 주요 서열 분석이 가능함을 보여주고 있다.