• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.555-566
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• 2017

For multivariate datasets with large number of variables, classical dimensional reduction methods such as principal component analysis may not be effective for data visualization. The underlying reason is that the dimensionality of the space of variables is often larger than two or three, while the visualization to the human eye is most effective with two or three dimensions. This paper proposes a working procedure which first partitions the variables into several "latent" clusters, explores individual data subsets, and finally integrates findings. We use R pakacage "ClustOfVar" for partitioning variables around latent dimensions and the principal component biplot method to visualize within-cluster patterns. Additionally, we use the technique for embedding supplementary variables to figure out the relationships between within-cluster variables and outside variables.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.61-74
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• 2015

Structural equation modeling (SEM) is a widely used in social sciences such as education, business administration, and psychology. In SEM, the latent variable score is the estimate of the latent variable which cannot be observed directly. This study uses stagewise structural equation modeling(stagewise SEM; SSEM) by partitioning the whole model into several stages. The traditional estimation method minimizes the discrepancy function using the variance-covariance of all observed variables. This method can lead to inappropriate situations where exogenous latent variables may be affected by endogenous latent variables. The SSEM approach can avoid such situations and reduce the complexity of the whole SEM in estimating parameters.

• The Korean Journal of Financial Studies
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• v.6 no.1
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• pp.223-247
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• 2000

본 연구에서는 유로커런시와 외환시장의 초과수익률을 설명하는 여러 가지 모형을 검증하였다. 비록 Campbell-Clarida(1987)와 Lewis(1990,1991)는 이 시장에서 3개월물의 초과수익률에 대한 단일잠재변수모형을 기각할 수 없었지만 본 연구에서는 이 모형이 여러 포트폴리오에 대하여 기각되고 있음을 보여준다. 그러나 이 모형의 검증은 결합가정을 필요로 하기 때문에 모형의 기각원인을 찾는다는 것은 어려운 일이다. 기각의 가능성으로 세계 경제에 하나 이상의 위험요인이 존재할 수 있다는 것이 될 수도 있고 불안정한 상수가 원인일 수가 있다. 상수의 안정성검증에서 1979년 12월을 전후한 기간에 상수가 변하지 않는다는 귀무가설이 모든 포트폴리오에서 기각되었다. 따라서 양기간에 단일잠재변수모형과 두잠재변수모형을 검증하였다. 모든 포트폴리오에 대하여 1979년 12월 이전에는 단일변수모형이 기각되지 않았지만, 1979년 이후에는 체계적으로 기각되었다. 한편 두잠재변수모형은 양기간 모두에서 기각되지 않았다 따라서 위험요인에 변화를 주는 연방준비은행의 운영절차의 변화가 단일변수모형의 기각의 원인일 수 있다고 유추할 수 있다. 마지막으로 시간 가변적인 베타가 단일변수모형의 기각의 원인이 될 수 있는지를 검증하기 위하여 Harvey(1989,1991)에 의해서 개발된 모형을 적용해 보았지만 모형이 기각되었다. 따라서 유로커런시와 외화자산의 3개월물의 초과수익률에 두 잠재변수모형이 자료를 비교적 잘 설명한다고 볼 수 있다. 그러나 잠재변수모형의 검증은 자산가격결정의 일반균형이론의 검증도 아니고 검증력도 강하지 않기 때문에, 위험프리미엄을 설명해주는 단순한 실증분석으로 보아야 한다.

• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.119-120
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• 2011

본 연구는 중소기업의 글로벌 혁신역량에 관련 선행연구를 조사분석한 연구이다. 국내외 중소기업의 글로벌혁신역량에 관한 선행연구를 광범위하게 조사한 후 중소기업의 글로벌 혁신역량에 영향을 미치는 대표적인 잠재변수를 선정한다. 각 잠재변수별로 유의적으로 판명된 지표를 분석하여 기업내부역량 중 글로벌혁신역량에 중요한 요인인 CEO역량, 기술역량, 마케팅역량, 생산역량, 인적자원역량, 재무역량, 네트워크역량으로 재분류한다. 향후 잠재변수사이의 상관관계를 분석하여 상호교호작용효과를 배제한 잠재변수별 순효과(net effect)를 분석하고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.231-242
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• 2007

This study was conducted to identify the relation of bottleneck and satisfaction factors in Korean succession companies. The final goal of this paper is finding some strategies and supporting system for Korean succession companies. According to the results of the study, we found four latent variables of the cause variables and two latent variables of the result variables. Three latent variables of the cause variables have an direct effect on two latent variables of the result variables.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1077-1095
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• 2014

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. Recently, new methods for the causal inference in the observational studies have been proposed such as the matching with the propensity score or the inverse probability treatment weighting. They have focused on how to control the confounders and how to evaluate the effect of the treatment on the result variable. However, these conventional methods are valid only when the treatment variable is categorical and both of the treatment and the result variables are directly observable. Research on the causal inference can be challenging in part because it may not be possible to directly observe the treatment and/or the result variable. To address this difficulty, we propose a method for estimating the average causal effect when both of the treatment and the result variables are latent. The latent class analysis has been applied to calculate the propensity score for the latent treatment variable in order to estimate the causal effect on the latent result variable. In this work, we investigate the causal effect of adolescents delinquency on their substance use using data from the 'National Longitudinal Study of Adolescent Health'.

• Journal of Korean Society of Transportation
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• v.28 no.4
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• pp.119-128
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• 2010

Mode choice behavior is associated with travelers' latent behavior that is an unobservable preference to travel behavior or mode characteristics. This paper specifically addresses the problem of unobservable factors, that is latent behavior, in mode choice models. Consideration of latent behavior in mode choice models reduces the errors that come from unobservable factors. In this study, the authors defined the latent variables that mean a quantitative latent behavior factors, and developed the combined RP/SP model with latent variables using the mode choice behavior survey data. The data has traveler's revealed preference of existent modes along the Han River and stated preference of new water transit on the Han River. Also, The data has travelers' latent behavior. Latent variables were defined by factor analysis using the latent behaviour data. In conclusion, it is significant that the relationship between traveler's latent behavior and mode choice behavior. In addition, the goodness-of-fit of the mode choice models with latent variables are better than the model without latent variables.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.615-632
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• 2017

Unlike randomized trial, statistical strategies for inferring the unbiased causal relationship are required in the observational studies. The matching with the propensity score is one of the most popular methods to control the confounders in order to evaluate the effect of the treatment on the outcome variable. Recently, new methods for the causal inference in latent class analysis (LCA) have been proposed to estimate the average causal effect (ACE) of the treatment on the latent discrete variable. They have focused on the application study for the real dataset to estimate the ACE in LCA. In practice, however, the true values of the ACE are not known, and it is difficult to evaluate the performance of the estimated the ACE. In this study, we propose a method to generate a synthetic data using the propensity score in the framework of LCA, where treatment and outcome variables are latent. We then propose a new method for estimating the ACE in LCA and evaluate its performance via simulation studies. Furthermore we present an empirical analysis based on data form the 'National Longitudinal Study of Adolescents Health,' where puberty as a latent treatment and substance use as a latent outcome variable.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.721-732
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• 2018

Latent class analysis (LCA) is an important tool to explore unseen latent groups in multivariate categorical data. In practice, it is important to select a suitable set of variables because the inclusion of too many variables in the model makes the model complicated and reduces the accuracy of the parameter estimates. Dean and Raftery (Annals of the Institute of Statistical Mathematics, 62, 11-35, 2010) proposed a headlong search algorithm based on Bayesian information criteria values to choose meaningful variables for LCA. In this paper, we propose a new variable selection procedure for LCA by utilizing posterior probabilities obtained from each fitted model. We propose a new statistic to measure the adequacy of LCA and develop a variable selection procedure. The effectiveness of the proposed method is also presented through some numerical studies.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.837-849
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• 2019

Latent class models (LCM) are useful tools to draw hidden information from categorical data. This model can also be interpreted as a mixture model with multinomial component distributions. In some cases, however, an available dataset may contain both categorical and count or continuous data. For such cases, we can extend the LCM to a mixture model with both multinomial and other component distributions such as normal and Poisson distributions. In this paper, we consider a LCM for the data containing categorical and count data to analyze the Drug Review dataset which contains categorical responses and text review. From this data analysis, we show that we can obtain more specific hidden inforamtion than those from the LCM only with categorical responses.