• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.119-127
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• 2009

It has been known that bus route and company related characteristics have influences on punctuality, but fewer research have been conducted. Independent variables used in this study were selected using correlation analysis, and OLS(Ordinary Least Square) and HLM(Hierarchical Linear Model) were employed to identify factors affecting bus punctuality(headway deviation). The results showed that ICC(intraclass Correlation Coefficient) is 0.10, indicating that hierarchical linear models are more adequate for these data because there is effective variation in the subjects between companies. Punctuality was found to be negatively associated with the number of vehicles, the number of persons per vehicle, and total travel time. On the other hand, average headway and company size have a positive relationship with punctuality. Therefore, the number of vehicles per route, average headway, and the number of vehicles managed by a company should be considered for more accurately evaluating the management of piunctuality.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1429-1440
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• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.101-109
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• 2015

SMRs (standardized mortality rates) for major diseases, accidents, cancer are considered in small areas of administrative units such as Eup/Myeon/Dong from years 2005 to 2008. Due to small sample issue in small areas, the precision of directly estimated crude SMR for each area can be low. In this study, we consider the HGLM (hierarchical generalized linear model) with MRF (Markov random field) to account for the spatial correlations among the small areas. The effects of covariates for cause of mortality by Dongs in Seoul and disease maps based on the estimated SMR are presented. The results suggest how we analyze and interpret the difference in mortalities by small areas such as Dongs by revealing the spatial patterns.

• MALSORI
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• no.33_34
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• pp.71-89
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• 1997

In the framework of linear representation which allows for no internal structure within features, there is no way to represent nonlinear phonological phenomena such as complex segments. This paper shows how we carl solve some problems of the linear feature theory in relation to the hierarchical feature theory. The purpose of this paper is to explain lateral assimilation under hierarchical feature representation. Although arguments for the position of classes of distinctive features have been made the position of (lateral) remains the issue of debate. Sagey(1988) argues that the feature [lateral] is structurally dependent on the root node. In contrast Rice & Avery (1991) put the feature (lated) under the spontaneous voicing. I have discussed previous studies of feature hierarchy and I propose a revised model of feature representation. Within this model I have shown how well feature geometry describes lateralization as feature spreading.

• The Mathematical Education
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• v.54 no.2
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• pp.195-206
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• 2015

The purpose of this study is to examine the effects of teacher on fourth-grade students' attitudes toward mathematics using data from TIMSS 2011. Students' attitudes toward mathematics included interest in learning mathematics, interest in mathematics lessons, and confidence in their mathematics ability. Teacher factors included mathematics professional development, confidence in teaching mathematics, teacher-centered mathematics instruction, and enhancing student mathematical thinking. The two level Hierarchical Linear Model was employed to analyze the relationship between teacher factors and student attitudes. Results showed that teacher-centered mathematics instruction significantly and positively predicted students' confidence about their mathematics ability. The findings suggest that school systems and mathematics educators need to provide teachers with the curriculum, assessment, and research-based practices and knowledge to overcome the obstacles to change their mathematics classroom.

• East Asian mathematical journal
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• v.33 no.4
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• pp.333-351
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• 2017

Schools are expected to ultimately moderate the difference of inequality issues among social groups and reduce the achievement gaps. This study investigates this expectation, in particular, how students' mathematics achievements are influenced by their parents' education at the individual level and by collective responsibility for teaching at the school level as well as the interaction of the two. Using a two-level hierarchical linear model, this study indicates that a school collective responsibility has a larger positive effect on students' mathematics achievement when their parents' education level is high. This means that school's collective responsibility accelerates inequity in students' mathematics achievement. Knowing that collective responsibility has less of an effect on students whose parents' education is not high, researchers, schools, and school districts should continue to search for school effects that have more of a positive impact on the relationship between mathematics achievement for students whose parents' education is not high in order to have more equitable results for all students.

• IE interfaces
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• v.23 no.1
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• pp.78-88
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• 2010

In this paper we develop a model for the performance evaluation of defense R&D test development projects based on analytic hierarchy process. First, evaluation indicators are collected through the related literature survey and a delphi inquiry method. Second, stepwise multiple linear regression is used for developing a hierarchical structure for analytic hierarchy process in the evaluation model, which can make the selected evaluation indicators of the hierarchical structure independent. Also we verify the effectiveness of proposed indicators of the performance evaluation by comparing with the existing evaluation indicators. The developed indicators for the performance evaluation is more reasonable and practical than the previous indicators on defense R&D test development projects.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.969-976
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• 2008

Mixed linear models have been widely used in various correlated data including multivariate survival data. In this paper we extend hierarchical-likelihood(h-likelihood) approach for mixed linear models with right censored data to that for left censored data. We also allow a general random-effect structure and propose the estimation procedure. The proposed method is illustrated using a numerical data set and is also compared with marginal likelihood method.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.65-75
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• 2015

This paper analyzes the determinants of wage for the college and university graduates utilizing both individual-level and industry-level variables. We note that wage determination has multi-level structure in the sense that individual wage is influenced by individual-level variables (level-1) and industry-level (level-2) variables. Then, the assumption that individual wage is independent in the classical regression is violated. Therefore, this paper utilizes the hierarchical linear model (HLM). The major results are the followings. First, the multiple correspondence analysis including level-1 and 2 variables reveals that both level 1 and level 2 variables affects individual wages judging from the fact that the values of level 1 and level 2 variables differ across the different level of individual wage groups. Second, the decision tree analysis including level-1 and 2 variables shows that the most influential variable in wage determination is industry-level wage and the next is industry-level working hour, ages and sex in the decling order in. This suggests that the utilization of the HLM is appropriate since the characteristics of industry is important in determining the individual wage. Third, it is shown that the HLM model is the best compared to the other models which do not take level-1 and level-2 variables simultaneously into account.

• Proceedings of the Korean Biophysical Society Conference
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• 2003.06a
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• pp.77-77
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• 2003

Gene expression in a cell is regulated by mutual activations or repressions between genes. Identifying the gene regulation network will be one of the most important research topics in the post genomic era. We propose a linear dynamic model of gene regulation for the yeast cell cycle. A small gene network consisting of about 40 genes is reconstructed from the analysis of micro-array gene expression data of yeast S. cerevisiae published by P. Spellman et al. We show that the network construction is consistent with the result of the hierarchical cluster analysis.