• 한국습지학회지
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• 제18권2호
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• pp.166-172
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• 2016

전 지구 지표 순환 분석을 위하여 지표와 대기 사이의 에너지 교환에 대한 분석이 필수적이다. 이러한 에너지 교환의 정량화를 위하여 다양한 지면 모형에 대한 연구가 진행되고 있다. 다양한 모형들 중 Common Land Model(CLM)과 Variable Infiltration Capacity(VIC) 모형을 활용한 연구가 활발히 수행되고 있다. CLM은 발전된 지면 모형의 형태로 적은 사용자 변수로 현실적인 결과를 산출한다는 장점이 있다. VIC 모형 또한 대표적인 지면 모형 중 하나로 에너지 인자 및 유출량 모의를 위하여 전 세계적으로 다양한 분야에서 활용되고 있다. 본 연구에서는 미국 캘리포니아 주 SS-CZO 사이트를 대상으로 CLM과 VIC 모형을 활용하여 주요 에너지 인자 인 순복사량, 현열, 잠열을 모의하였다. 순복사량과 현열 모두 두 모형에서 양호한 결과를 보이나, 강우 발생 시 CLM은 잠열과 현열을 과소모의하는 경향을 나타내었다. 잠열은 CLM의 모의 결과가 잠열을 과소모의 한 VIC 모형에 비하여 관측된 잠열의 경향을 더 잘 모의하는 것으로 나타났다. 이러한 에너지 인자 모의 및 모형의 장단점에 대한 분석을 통하여 CLM과 VIC 모형의 활용가능성 및 다양한 모형 활용의 필요성을 확인하였다.

• 한국산업정보학회논문지
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• 제22권6호
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• pp.71-84
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• 2017

Over the past decade, we have witnessed Serious Debates in MISQ and Other Journals Between Two Camps that have Differing Views on the use of Causal Indicators to Measure Constructs. There is the Camp that advocates Causal Indicators (ADVOCATE) and the Camp that opposes Their Usage (OPPONENT). The Debates have been primarily centered on the OPPONENT's Argument that the Meaning of a Latent Variable is determined by its Outcome Variables. However, Little Effort has been made to Validate the ADVOCATE's Dispute (Against the OPPONENT's Arguments) that the Meaning of a Latent Variable is decided by its Causal Indicators if there is no Misspecification. Our Study precisely examines the Integrity of the Argument. For this, we empirically examine how the two Primary Psychometric Properties-Comprehensiveness and Interrelationship-of Causal Indicators Influence Theory Testing between Latent Variables through Three Different Tests (i.e., Comprehensive Test, Interrelationship Test, and Mixed Test). Conducted on Two Different Datasets, Our Analysis Consistently Reveals that Structural Path Coefficients are Hardly Sensitive to the Changes (i.e., Misspecification) in the Properties of Causal Indicators. The Discovery offers Important Evidence that the Sound Theoretical Logic of a Causal Model is not in Sync with the Empirical Mechanism of Parameter Estimation. This Underscores that a Latent Variable Formed by Causal Indicators is empirically an elusive notion that is Difficult to Operationalize. As Our Results have Significant Implications on the Integrity of Numerous IS studies which have conducted Theory or Hypothesis Testing Using Causal Indicators, we strongly advocate Open Discussions among Methodologists regarding Our Findings and Their Implications for Both Published IS Research and Future Practices.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.633-636
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• 2006

In order to determine new settings of key process variables optimally, a new rule induction method through a historical data is proposed without using an explicit functional model between process and quality variables. First, a partial least square is used to reduce the dimensionality of the process variables. Then new process settings that yield the best quality variable are identified by sequentially partitioning the reduced latent variable space using a patient rule induction method. The proposed method is illustrated with a case study obtained from steel-making processes. We also show, through simulation, that the proposed method gives more stable results than estimating an explicit function even when the form of the function is known in advance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권1호
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• pp.81-98
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• 2013

Since Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) were introduced, many revised or extended topic models have appeared. Due to the intractable likelihood of these models, training any topic model requires to use some approximation algorithm such as variational approximation, Laplace approximation, or Markov chain Monte Carlo (MCMC). Although these approximation algorithms perform well, training a topic model is still computationally expensive given the large amount of data it requires. In this paper, we propose a new method, called non-simultaneous sampling deactivation, for efficient approximation of parameters in a topic model. While each random variable is normally sampled or obtained by a single predefined burn-in period in the traditional approximation algorithms, our new method is based on the observation that the random variable nodes in one topic model have all different periods of convergence. During the iterative approximation process, the proposed method allows each random variable node to be terminated or deactivated when it is converged. Therefore, compared to the traditional approximation ways in which usually every node is deactivated concurrently, the proposed method achieves the inference efficiency in terms of time and memory. We do not propose a new approximation algorithm, but a new process applicable to the existing approximation algorithms. Through experiments, we show the time and memory efficiency of the method, and discuss about the tradeoff between the efficiency of the approximation process and the parameter consistency.

• Journal of the Korean Statistical Society
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• 제30권3호
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• pp.435-451
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• 2001

We describe a hierarchical bayesian model to analyze multinomial nonignorable nonresponse data. Using a Dirichlet and beta prior to model the cell probabilities, We develop a complete hierarchical bayesian analysis for multinomial proportions without making any algebraic approximation. Inference is sampling based and Markove chain Monte Carlo methods are used to perform the computations. We apply our method to the dta on body mass index(BMI) and show the model works reasonably well.

• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.407-420
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• 1998

This paper is concerned with selecting covariates to be included in building linear random effects models designed to analyze clustered response normal data. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting premising subsets of covariates. The approach reformulates the linear random effects model in a hierarchical normal and point mass mixture model by introducing a set of latent variables that will be used to identify subset choices. The hierarchical model is flexible to easily accommodate sign constraints in the number of regression coefficients. Utilizing Gibbs sampler, the appropriate posterior probability of each subset of covariates is obtained. Thus, In this procedure, the most promising subset of covariates can be identified as that with highest posterior probability. The procedure is illustrated through a simulation study.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.155-166
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• 2002

Neural networks have been studied as a popular tool for classification and they are very flexible. Also, they are used for many applications of pattern classification and pattern recognition. This paper focuses on Bayesian approach to feed-forward neural networks with single hidden layer of units with logistic activation. In this model, we are interested in deciding the number of nodes of neural network model with p input units, one hidden layer with m hidden nodes and one output unit in Bayesian setup for fixed m. Here, we use the latent variable into the prior of the coefficient regression, and we introduce the 'sequential step' which is based on the idea of the data augmentation by Tanner and Wong(1787). The MCMC method(Gibbs sampler and Metropolish algorithm) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data.

• The Journal of Asian Finance, Economics and Business
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• 제9권2호
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• pp.121-131
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• 2022

The goal of the study was to see how quality management (QMA), strategy (STR), and innovative capability (INC) influence the competitive advantage of a Thai information communication technology (ICT) firm (COA). The researchers collected 431 surveys from Thailand's owners and managers employed in ICT enterprises from the beginning of June 2021 to the end of September 2021using diverse sample strategies. A questionnaire with an index of item-objective congruence (IOC) value of 0.60-1.00 and a reliability value of 0.92-0.96 was used as the research tool. Participants in the survey were requested to fill out a seven-level opinion survey posted on Google Forms. A latent variable structural equation model (SEM) path analysis using LISREL 9.1 was used for the four latent variables, 31 manifest variables, and the five hypotheses testing. The analysis showed that all three causal variables positively affected COA, which had a total effect (TE) R² value = 80% when combined with the other latent variables. Moreover, the values for the latent variables when ranked by total effect (TE) were STR, QMA, and INC with TE values of 0.95, 0.89, and 0.25, respectively. Finally, there were very strong influences from COA to STR (0.95), INC to QMA (0.86), and STR to QMA (0.71).

• Asian Pacific Journal of Cancer Prevention
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• 제16권17호
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• pp.7923-7927
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• 2015

Background: The Cox PH model is one of the most significant statistical models in studying survival of patients. But, in the case of patients with long-term survival, it may not be the most appropriate. In such cases, a cure rate model seems more suitable. The purpose of this study was to determine clinical factors associated with cure rate of patients with breast cancer. Materials and Methods: In order to find factors affecting cure rate (response), a non-mixed cure rate model with negative binomial distribution for latent variable was used. Variables selected were recurrence cancer, status for HER2, estrogen receptor (ER) and progesterone receptor (PR), size of tumor, grade of cancer, stage of cancer, type of surgery, age at the diagnosis time and number of removed positive lymph nodes. All analyses were performed using PROC MCMC processes in the SAS 9.2 program. Results: The mean (SD) age of patients was equal to 48.9 (11.1) months. For these patients, 1, 5 and 10-year survival rates were 95, 79 and 50 percent respectively. All of the mentioned variables were effective in cure fraction. Kaplan-Meier curve showed cure model's use competence. Conclusions: Unlike other variables, existence of ER and PR positivity will increase probability of cure in patients. In the present study, Weibull distribution was used for the purpose of analysing survival times. Model fitness with other distributions such as log-N and log-logistic and other distributions for latent variable is recommended.

• 한국과학교육학회지
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• 제17권3호
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• pp.301-311
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• 1997

The purpose of this study is to construct a structural equation model and to analyze causal relationships among variables related to attitudes to science using structural equation modeling(SEM) with LISREL VII. The sample consisted of 483 10th grade boys from a general high school in Pusan, Korea. The questionnaires (ABC-attitude scale: affection, behavioral intention, cognition scale of attitude towards science) were developed by the researcher through a pilot study. And other instruments have modified previous ones. Five instruments were used in this study: GALT(group assessment of logical thinking), MTSlS(modified test of science inquiry skill), ABC-attitude scale, MSAS(modified scientific attitude scale), CSAT(common science achievement test). Structural equation modeling with LISREL VII($J\ddot{o}reskog$ & $S\ddot{o}rbom,$ 1993) was employed to estimate the causal inferences about hypothesized relationships among observed data sets. Three competing models consisted of five latent variable(scientific thinking ability, science inquiry skill, attitude towards science, scientific attitude, science achievement) - lP(inquiry preceding) model, AP(attitude preceding) model and AM(attitude mediating) model - were developed. Among these competing models, IP model satisfied the observed data sets. The causal relationships among "attitudes to science" and other latent variables were reliably identified. According to the results of the present study, science inquiry skill was the most significant variable that can predict science achievement. But scientific thinking ability has not directly influenced science achievement. This study suggests that inquiry based teaching-learning processes should be offered to students for improvement of science achievement. At the same time, it seems to be important to develop positive attitude towards science. Understanding of relationships among variables related to attitudes to science will be helpful to the development of science curriculum and to the design of science teaching and learning process. LISREL has been recognized as a useful approach in testing a SEM. However, in this study, LISREL approach was estimated as much more useful method for research design.