• International Journal of Highway Engineering
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• v.18 no.4
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• pp.83-92
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• 2016

PURPOSES : The purpose of this study was to develop safety performance functions (SPFs) that use zero-inflated negative binomial regression models for urban intersections in central business districts (CBDs), and to compare the statistical significance of developed models against that of regular negative binomial regression models. METHODS : To develop and analyze the SPFs of intersections in CBDs, data acquisition was conducted for dependent and independent variables in areas of study. We analyzed the SPFs using zero-inflated negative binomial regression model as well as regular negative binomial regression model. We then compared the results by analyzing the statistical significance of the models. RESULTS : SPFs were estimated for all accidents and injury accidents at intersections in CBDs in terms of variables such as AADT, Number of Lanes at Major Roads, Median Barriers, Right Turn with an Exclusive Turn Lane, Turning Guideline, and Front Signal. We also estimated the log-likelihood at convergence and the likelihood ratio of SPFs for comparing the zero-inflated model with the regular model. In he SPFs, estimated log-likelihood at convergence and the likelihood ratio of the zero-inflated model were at -836.736, 0.193 and -836.415, 0.195. Also estimated the log-likelihood at convergence and likelihood ratio of the regular model were at -843.547, 0.187 and -842.631, 0.189, respectively. These figures demonstrate that zero-inflated negative binomial regression models can better explain traffic accidents at intersections in CBDs. CONCLUSIONS : SPFs that use a zero-inflated negative binomial regression model demonstrate better statistical significance compared with those that use a regular negative binomial regression model.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2956-2958
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• 1999

The classical solution to the noise removal problem is the Wiener filter, which utilizes the second-order statistics of the Fourier decomposition. We discuss a Bayesian formalism which gives rise to a type of wavelet threshold estimation in non-parametric regression. A prior distribution is imposed on the wavelet coefficients of the unknown response function, designed to capture the sparseness of wavelet expansion common to most application. For the prior specified, the posterior median yields a thresholding procedure

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.10a
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• pp.383-386
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• 2009

Contract power conversion factor which is applied to estimate contract power of industrial customers is an important standard to calculate transformer capacity. This paper shows a reasonable contract power conversion factor, that was made by the systematic and statistical way considering actual conditions, such as investigated contract power and peak power for the last 5 years of each customer for industrial customers as to AMR system. In this dissertation, it is necessary to analyze the key features and general trend from the investigated data. It made an analysis of the feature parameters, such as average, standard deviation, median, maximum. minimum and thus it was carried the linear and nonlinear regression analysis. Therefore, this paper compared characteristics for a contract power conversion factor which is applied to calculate contract power with characteristics for a regression model for customers which maximum utilization factor of transformer is more than 60%.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.05a
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• pp.331-336
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• 2005

It is increased electrical energy consumption with the development of intelligence society in the office buildings and thus an energy conservation through efficient use of electricity became more important. This paper shows a reasonable design demand factor in office buildings, that was made by the systematic and statistical way considering actual conditions, such as investigated electric equipment capacity, peak power consumption, demand factor, etc., for 54 office buildings and 34 electrical design offices. In this dissertation, it is necessary to analyse the key features and general trend from the investigated data. It made an analysis of the feature parameters, such as average, standard deviation, median, maximum, minimun and thus it was carried linear and nonlinear regression analysis.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.337-346
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• 2001

In this paper we consider the problem of identifying and testing the outliers in linear regression. first we consider the problem for testing the null hypothesis of no outliers. The test based on the ratio of two scale estimates is proposed. We show the asymptotic distribution of the test statistic by Monte Carlo simulation and investigate its properties. Next we consider the problem of identifying the outliers. A forward sequential procedure based on the suggested test is proposed and shown to perform fairly well. The forward sequential procedure is unaffected by masking and swamping effects because the test statistic is based on robust estimate.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.05a
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• pp.241-244
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• 2007

It is increased electrical energy consumption with the development of intelligence society in office buildings and thus an energy conservation through efficient use of electricity became more important. This paper shows a reasonable design demand factor in office buildings, that was made by the systematic and statistical way considering actual conditions, such as investigated electric equipment capacity, peak power consumption, demand factor, etc., for 132 office buildings. In this dissertation, it is necessary to analyze the key features and general trend from the investigated data. It made an analysis of the feature parameters, such as average, standard deviation, median, maximum, minimun and thus it was carried linear and nonlinear regression analysis.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.7
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• pp.974-988
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• 1995

In this paper, we propose a range image segmentation algorithm using robust regression. We derive a least $\kappa$th-order square (LKS) method by generalizing the least median of squares (LMedS) method and compare it with the conventional robust regressions. The LKS is robuster against outliers than the LMedS and shows performance similar to the residual consensus (RESC). The RESC uses the predetermined number of sorted residuals, whereas the LKS uses an adaptive parameter determined by given observations rather than the a priori knowledge. Computer simulation with synthetic and real range images shows that the proposed LKS algorithm gives better performance than the conventional ones.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.293-296
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• 2008

Contract power conversion factor which is applied to estimate contract power of general customers IS an important standard to caculate transformer capacity. This paper shows a reasonable contract power conversion factor, that was made by the systematic and statistical way considering actual conditions, such as investigated contract power and peak power for the last 5 years of each customer for 132 office building customers as to AMR system. In this dissertation, it is necessary to analyze the key features and general trend from the investigated data. It made an analysis of the feature parameters, such as average, standard deviation, median, maximum, minimun and thus it was carried the linear and nonlinear regression analysis. Therefore, this paper compared characteristics for a contract power conversion factor which is applied to calculate contract power with characteristics for a regression model for customers which maximum utilization factor of transformer is more than 60%.

• Journal of Korean Society for Quality Management
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• v.43 no.3
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• pp.409-420
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• 2015

Purpose: This study is to estimate the life of elevator wire rope by using the accelerated degradation test with two accelerating variables of applied load and corrosion. Methods: Linear regression method is used to find the pseudo life of elevator wire rope at each combination of accelerating variables and the median life of elevator wire rope at use condition is estimated under the assumption that the life of elevator wire rope follows lognormal distribution. Results: The particular case study demonstrated that the results of the elevator wire life estimation by using the proposed method can provide the better solutions than existing methods. Conclusion: It can be economical to use accelerated degradation model for estimating the life of elevator wire rope.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.291-304
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• 2002

This article is concerned with an effective algorithm for the identification of multiple outliers in linear regression. It proposes a hybrid algorithm which employs the least median of squares estimator, instead of the least squares estimator, to construct an Initial clean subset in the stepwise forward search scheme. The performance of the proposed algorithm is evaluated and compared with the existing competitor via an extensive Monte Carlo simulation. The algorithm appears to be superior to the competitor for the most of scenarios explored in the simulation study. Particularly it copes with the masking problem quite well. In addition, the orthogonal decomposition and Its updating techniques are considered to improve the computational efficiency and numerical stability of the algorithm.