• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.143-154
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• 2017

A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.44-57
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• 2019

It is crucial to understand how people make decisions on mode choice and to accurately predict their behaviors in transportation planning. One of avenues for advancing modeling is, in particular, taking into account for taste heterogeneity in modeling that can incorporate different decision-making processes across group. In this study, we hypothesize that how people make decisions on mode choice would differ by destination in that land use characteristics are heterogeneous by zone even if zones are all in the same area. To this end, we apply Latent Class Modeling (LCM) to commute trips in Seoul by using 2010 household travel diary survey, investigate types of latent classes with the aid of characteristics of destination, and analyze how those classes differently response to factors. The LCM identifies two classes: in the first one, modal split of auto and public transit (bus and metro) is almost half-and-half and the trip destinations are characterized by relatively more residence facilities and less business/commercial facilities; in the second one, public transit has a notably high share and trip destinations are characterized by relatively more business/commercial facilities. In addition, it turns out that demographic and socio-economic variables affect mode choice differently by class.

• 한국노년학
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• v.31 no.3
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• pp.733-749
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• 2011

The purpose of this research are as follows. The first is to investigate the mediating effect of interpersonal skill on the relationship between the ability to use Internet and social activity among the 50's and 60's. The second is to compare 60's latent mean of core variables with 50's ones in terms of information gap. The data was Seoul welfare panel data made by Seoul welfare foundation in 2008 and the total subject was 941(50's=644, 60's=297). When it comes to the research methods, structured equation analysis for verifying the mediating effect and latent mean analysis for comparing the two groups were practiced. The results of this research are as follows. First of all, interpersonal skill did function as partial mediator. Second, according to the latent mean analysis, the group of 50's showed a more higher level of the ability to use Internet. On the other hand, the group of 60's revealed a more active social participation. In conclusion, Information educational programs should focus on Internet communication skill for enhancing social activities of the elderly and consider the differentiations among the elder generations.

• Korean Journal of Culture and Social Issue
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• v.25 no.4
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• pp.325-351
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• 2019

The present study examined patterns of co-occurrence between DSM-5 posttraumatic stress disorder(PTSD) symptoms and posttraumatic growth(PTG) among Korean populations(n= 860). Latent profile analysis was used to identify subclasses and suggested that the 3-class model fit best: (1) Low PTSD/Mild PTG group (2) Low PTSD/High PTG group; (3) High PTSD/High PTG group. Class membership was predicted by demographic variables, social isolation, and frequency of traumatic experiences. Classes also differed with respect to self-destructive behaviors(binge eating, non-suicidal self-injury, and problem drinking). These findings contribute to future research about the coexisting patterns of PTSD and PTG, and to identify high-risk individuals who suffer from trauma-related problems in clinical practice.

• The Mathematical Education
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• v.63 no.1
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• pp.19-33
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• 2024

This study investigates longitudinal patterns in middle school students' mathematics interest and achievement using panel data from the 4th to 6th year of the Gyeonggi Education Panel Study. Results from the multivariate growth mixture model confirmed the existence of heterogeneous characteristics in the longitudinal trajectory of students' mathematics interest and achievement. Students were classified into four latent classes: a low-level class with weak interest and achievement, a high-level class with strong interest and achievement, a middlelevel-increasing class where interest and achievement rise with grade, and a middle-level-decreasing class where interest and achievement decline with grade. Each class exhibited distinct patterns in the change of interest and achievement. Moreover, an examination of the correlation between intercepts and slopes in the multivariate growth mixture model reveals a positive association between interest and achievement with respect to their initial values and growth rates. We further explore predictive variables influencing latent class assignment. The results indicated that students' educational ambition and time spent on private education positively affect mathematics interest and achievement, and the influence of prior learning varies based on its intensity. The perceived instruction method significantly impacts latent class assignment: teacher-centered instruction increases the likelihood of belonging to higher-level classes, while learner-centered instruction increases the likelihood of belonging to lower-level classes. This study has significant implications as it presents a new method for analyzing the longitudinal patterns of students' characteristics in mathematics education through the application of the multivariate growth mixture model.

• Journal of Labour Economics
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• v.38 no.1
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• pp.1-30
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• 2015

The aggregate matching function is estimated to explain dynamics among job seekers, vacancies and new hires in Korea. Due to measurement errors inherent in vacancies data, I introduce a latent variable for job openings and use the instrumental variables to correct its endogeneity. Matching efficiency is also estimated using some explanatory variables like job seekers' characteristics and public employment services. The result shows that Korea's matching function also exhibits a constant returns to scale.

• Journal of Information Technology Applications and Management
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• v.23 no.3
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• pp.25-34
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• 2016

Often we receive misled conclusion in the research if properly variables are not analyzed. In different functional issues of management it is very essential that all the latent and observed variable are properly understood so management decisions will be relevant and effective. The objective of this paper is to investigate the use of different multivariate tools for analyzing in the management research : applied or basic. The sources of data is primary as well as secondary. The primary includes the observation of different research articles of the proceedings of different conferences. And the secondary includes different publications related to multivariate analysis. The study has revealed the reasons of not using such tools of research. The preliminary finding reveals that most of the researches do not use such analytical tools in a comprehensive manner. Carelessness in design while fixing the design aspect is the main reasons of not using appropriate design.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1998.03a
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• pp.179-184
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• 1998

Semi-solid forging can be applied in industry only with enough knowledge of the effects of the forming parameters related with the process and their exact control which can be obtained by empirical or numerical methods. In the current study, the effects of process variables on semi-solid forging are discussed based on mainly numerical results. Die preheating temperature, initial solid fraction of the workpiece, and die velocity were selected as process variables, and numerical analyses using a rigid-thermoviscoplastic finite element approach that considered the release of latent heat due to phase change were carried out. In the analyses, a proposed flow stress material characterization and a solid fraction updating algorithm were employed. The obtained results from numerical analysis are discussed and are compared with some experimental observations.

• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.439-454
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• 2013

This study presents a Bayesian multiple change-point detection approach to segment and classify the observations that no longer come from an initial population after a certain time. Inferences are based on the multiple change-points in a sequence of random variables where the probability distribution changes. Bayesian multiple change-point estimation is classifies each observation into a segment. We use a truncated Poisson distribution for the number of change-points and conjugate prior for the exponential family distributions. The Bayesian method can lead the unsupervised classification of discrete, continuous variables and multivariate vectors based on latent class models; therefore, the solution for change-points corresponds to the stochastic partitions of observed data. We demonstrate segmentation with real data.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2039-2042
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• 2008

Recently, Electronic & Electrical Products have problems how to reduce heat in trend reducing size and increasing speed. heat pipes worked by latent heats can solve problems for effective and quiet electronic applications. Heat Pipes have to be suitably designed for the external conditions due to showing optimum performance. it has influence on efficiency of heat pipes to the exterior structure changed by length, bending angle, diameter. Designing heat pipes has depended on experience from trial and error. this method wasted too many resources, but can't guarantee efficiency. to prevent those wastes, this study aims at making the thermal transfer coefficient predicting efficiency. In this study, the thermal transfer coefficient has been made from experimental results that used variables - lengths between heat source and radiation, bending angles, diameters of heat pipes. variables become non-dimensional in modeling process for making the coefficient.