• Journal of the Ergonomics Society of Korea
• /
• v.22 no.4
• /
• pp.59-78
• /
• 2003

Most regression models of anthropometric variables use stature and/or weight as regressors; however, these 'flat' regression models result in large errors for anthropometric variables having low correlations with the regressors. To develop more accurate regression models for anthropometric variables, this study proposed a method to estimate anthropometric variables in a hierarchical manner based on the relationships among the variables and a process to develop and improve corresponding regression models. By applying the proposed approach, a hierarchical estimation structure was constructed for 59 anthropometric variables selected for the occupant package design of a passenger car and corresponding regression models were developed with the 1988 US Army anthropometric survey data. The hierarchical regression models were compared with the corresponding flat regression models in terms of accuracy. As results, the standard errors of the hierarchical regression models decreased by 28% (4.3mm) on average compared with those of the flat models.

• International Journal of Highway Engineering
• /
• v.14 no.5
• /
• pp.133-143
• /
• 2012

PURPOSES: Using the collected data for crash, traffic volume, and design elements on ramps between 2007 and 2009, this research effort was initiated to develop traffic crash prediction models for expressway ramps. METHODS: Three negative binomial regression models and three zero-inflated negative binomial regression models were developed for individual ramp types, including direct, semi-direct and loop, respectively. For validating the developed models, authors compared the estimated crash frequencies with actual crash frequencies of twelve randomly selected interchanges, the ramps of which have not been used for model developing. RESULTS: The results show that the negative binomial regression models for direct, semi-direct and loop ramps showed 60.3%, 63.8% and 48.7% error rates on average whereas the zero-inflated negative binomial regression models showed 82.1%, 120.4% and 57.3%, respectively. CONCLUSIONS: Conclusively, the negative binomial regression models worked better in traffic crash prediction than the zero-inflated negative binomial regression models for estimating the frequency of traffic accidents on expressway ramps.

• Communications for Statistical Applications and Methods
• /
• v.13 no.1
• /
• pp.125-134
• /
• 2006

This study confirms that the regression model of endpoint of interval outputs is not identical with that of the other endpoint of interval outputs in interval regression models proposed by Tanaka et al. (1987) and constructs interval regression models using the best regression model given by variable selection. Also, this paper suggests a method to minimize the sum of lengths of a symmetric difference among observed and predicted interval outputs in order to estimate interval regression coefficients in the proposed model. Some examples show that the interval regression model proposed in this study is more accuracy than that introduced by Inuiguchi et al. (2001).

• International Journal of Highway Engineering
• /
• v.14 no.4
• /
• pp.83-91
• /
• 2012

PURPOSES : This study deals with Rotary by Accident Occurrence Location. The purpose of this study is to develop the accident models of rotary by location. METHODS : In pursuing the above, this study gives particular attentions to developing the appropriate models using multiple linear, Poisson and negative binomial regression models and statistical analysis tools. RESULTS : First, four multiple linear regression models which are statistically significant(their $R^2$ values are 0.781, 0.300, 0.784 and 0.644 respectively) are developed, and four Poisson regression models which are statistically significant(their ${\rho}^2$ values are 0.407, 0.306, 0.378 and 0.366 respectively) are developed. Second, the test results of fitness using RMSE, %RMSE, MPB and MAD show that Poisson regression model in the case of circulatory roadway, pedestrian crossing and others and multiple linear regression model in the case of entry/exit sections are appropriate to the given data. Finally, the common variable that affects to the accident is adopted to be traffic volume. CONCLUSIONS : 8 models which are all statistically significant are developed, and the common and specific variables that are related to the models are derived.

• Journal of Korea Artificial Intelligence Association
• /
• v.1 no.1
• /
• pp.11-16
• /
• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Journal of the Korean Data and Information Science Society
• /
• v.13 no.1
• /
• pp.157-164
• /
• 2002

In this paper, we consider the strong consistency of the regression quantiles estimators for the nonlinear regression models when dependent variables are subject to censoring, and provide the sufficient conditions which ensure the strong consistency of proposed estimators of the censored regression models. one example is given to illustrate the application of the main result.

• Communications for Statistical Applications and Methods
• /
• v.26 no.4
• /
• pp.359-370
• /
• 2019

Grouping structures in covariates are often ignored in regression models. Recent statistical developments considering grouping structure shows clear advantages; however, reflecting the grouping structure on the quantile regression model has been relatively rare in the literature. Treating the grouping structure is usually conducted by employing a group penalty. In this work, we explore the idea of group penalty to the quantile regression models. The grouping structure is assumed to be known, which is commonly true for some cases. For example, group of dummy variables transformed from one categorical variable can be regarded as one group of covariates. We examine the group quantile regression models via two real data analyses and simulation studies that reveal the beneficial performance of group quantile regression models to the non-group version methods if there exists grouping structures among variables.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.30 no.4
• /
• pp.28-36
• /
• 2002

The main purpose of this study was to estimate a new regression model to explain the relationship between urban forest and air temperature in summer, 2001. This study consists of two parts: correlation coefficient analysis and regression analysis. According to correlation coefficient analysis, thermal infra-red radiations of the major land use categories found significant difference in each category. However there were no significant relationship between the data (thermal infra-red radiation and NDVI) derived from Landsat-7 ETM+ image and air temperature at Automatic Weather Stations(AWSs). After estimating various regression models for summer air temperature, the final models were chosen. The final regression models consisted of two variables such as forest m and traffic facilities area. The regression models explained over 78% of the variability in air temperatures. The regression models with variables of forest area and traffic facilities area showed that the coefficient of the first variable was even more significant than the second one. However, the negative impact of the traffic facilities area was slightly greater than the positive impact of the forest area. Consequently, the effects of forest area and traffic facilities area were apparent to explain summer air temperature in Seoul. Therefore two policies have the most important implications to mitigate the summer air temperature in Seoul: to expand and to conserve the urban forest; and to change the Oafnc facilities'characteristics. The results from this study are expected to be useful not merely in informing the public that urban forest mitigates summer air temperahne, but in urging the necessity of budgets for trees and managing urban forests. It is recommended that field swey of summer air temperature be Performed for the vadidation of the models. The main purpose of this study was to estimate a new regression model to explain the relationship between urban forest and air temperature in summer, 2001. This study consists of two parts: correlation coefficient analysis and regression analysis. According to correlation coefficient analysis, thermal infra-red radiations of the major land use categories found significant difference in each category. However there were no significant relationship between the data (thermal infra-red radiation and NDVI) derived from Landsat-7 ETM+ image and air temperature at Automatic Weather Stations(AWSs). After estimating various regression models for summer air temperature, the final models were chosen. The final regression models consisted of two variables such as forest m and traffic facilities area. The regression models explained over 78% of the variability in air temperatures. The regression models with variables of forest area and traffic facilities area showed that the coefficient of the first variable was even more significant than the second one. However, the negative impact of the traffic facilities area was slightly greater than the positive impact of the forest area. Consequently, the effects of forest area and traffic facilities area were apparent to explain summer air temperature in Seoul. Therefore two policies have the most important implications to mitigate the summer air temperature in Seoul: to expand and to conserve the urban forest; and to change the traffic facilities'characteristics. The results from this study are expected to be useful not merely in informing the public that urban forest mitigates summer air temperature, but in urging the necessity of budgets for trees and managing urban forests. It is recommended that field survey of summer air temperature be Performed for the vadidation of the models.

• Computers and Concrete
• /
• v.21 no.4
• /
• pp.463-470
• /
• 2018

In the present study, soft computing i.e., machine learning techniques and regression models algorithms have earned much importance for the prediction of the various parameters in different fields of science and engineering. This paper depicts that how regression models can be implemented for the prediction of compressive strength of concrete. Three models are taken into consideration for this; they are Gaussian Process for Regression (GPR), Multi Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age in days have been taken as inputs and compressive strength as output for GPR, MARS and MPMR models. A comparatively large set of data including 1030 normalized previously published results which were obtained from experiments were utilized. Here, a comparison is made between the results obtained from all the above mentioned models and the model which provides the best fit is established. The experimental results manifest that proposed models are robust for determination of compressive strength of concrete.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.2
• /
• pp.515-524
• /
• 2007

This paper deals with local linear estimation of fuzzy regression models based on Diamond(1998) as a new class of non-linear fuzzy regression. The purpose of this paper is to introduce a use of smoothing in testing for lack of fit of parametric fuzzy regression models.