• Communications for Statistical Applications and Methods
• /
• v.15 no.1
• /
• pp.27-41
• /
• 2008

A graphical method of checking the adequacy of a generalized linear model is proposed. The graph helps to assess the assumption that the link function of mean can be expressed as a linear combination of explanatory variables in the generalized linear model. For the graph the boosting technique is applied to estimate nonparametrically the relationship between the link function of the mean and the explanatory variables, though any other nonparametric regression methods can be applied. Through simulation studies with normal and binary data, the effectiveness of the graph is demonstrated. And we list some limitations and technical details of the graph.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.2
• /
• pp.337-343
• /
• 2003

In this paper we present the prediction interval estimation method using bootstrap method for least squares support vector machine(LS-SVM) regression, which allows us to perform even nonlinear regression by constructing a linear regression function in a high dimensional feature space. The bootstrap method is applied to generate the bootstrap sample for estimation of the covariance of the regression parameters consisting of the optimal bias and Lagrange multipliers. Experimental results are then presented which indicate the performance of this algorithm.

• Communications for Statistical Applications and Methods
• /
• v.19 no.4
• /
• pp.579-590
• /
• 2012

Local linear jump detection in a discontinuous regression function involves the choice of the bandwidth and the performance of a local linear jump detector depends heavily on the choice of the bandwidth. However, little attention has been paid to this important issue. In this paper we propose two fully data adaptive bandwidth selection methods for a local linear jump detector. The performance of the proposed methods are investigated through a simulation study.

• Communications for Statistical Applications and Methods
• /
• v.18 no.2
• /
• pp.165-170
• /
• 2011

Support vector quantile regression(SVQR) is capable of providing a good description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse SVQR to overcome a limitation of SVQR, nonsparsity. The asymmetric e-insensitive loss function is used to efficiently provide sparsity. The experimental results are presented to illustrate the performance of the proposed method by comparing it with nonsparse SVQR.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.10 no.3
• /
• pp.233-243
• /
• 2008

A tunnel behavior predicted in the investigation and design stage is often different from its actual behavior due to mainly the complexity of ground conditions. In a tunnel construction, therefore, it is necessary to ensure the stability of the tunnel by predicting the behaviors of the ground and the supports through observations and measurements, and modifying immediately excavation and reinforcing methods when necessary. To do so, it is important to be able to predict the final tunnel behavior based on the initial tunnel behavior as early as possible. In this study, the correlations were obtained between the initial and the final convergence by analyzing statistically the convergence measurement data, collected from two domestic road tunnels under construction using NATM. In order to estimate the unknown displacements, occurred during the period between the excavation and the first measurement, two methods were used - one is the method by means of regression analysis using a modified exponential function and the other the method by a simple linear regression analysis using the data measured within the distance from tunnel face equal to the tunnel diameter (D). Finally, the relationships were obtained between the initial and final convergence, including the non-measured displacements estimated from the two different methods, by performing linear regression analyses. The regression analysis results showed that there are clear linear relationships between the initial and final convegence and the difference between the two linear regression equations was not that large for when using the exponential function and the simple linear function to estimate the non-measured displacements.

• Fire Science and Engineering
• /
• v.28 no.2
• /
• pp.69-75
• /
• 2014

This study derives the factors which affect the occurrence of arson from statistical data (population, economic, and social factors) by multiple regression analysis. Multiple regression analysis applies to 4 forms of functions, linear functions, semi-log functions, inverse log functions, and dual log functions. Also analysis respectively functions by using the stepwise progress which considered selection and deletion of the independent variable factors by each steps. In order to solve a problem of multiple regression analysis, autocorrelation and multicollinearity, Variance Inflation Factor (VIF) and the Durbin-Watson coefficient were considered. Through the analysis, the optimal model was determined by adjusted Rsquared which means statistical significance used determination, Adjusted R-squared of linear function is scored 0.935 (93.5%), the highest of the 4 forms of function, and so linear function is the optimal model in this study. Then interpretation to the optimal model is conducted. As a result of the analysis, the factors affecting the arson were resulted in lines, the incidence of crime (0.829), the general divorce rate (0.151), the financial autonomy rate (0.149), and the consumer price index (0.099).

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.3
• /
• pp.527-535
• /
• 2008

Many signal processing techniques have been described in the literature for estimating amplitude, frequency and duration variables of the surface EMG signal detected during constant voluntary contractions. They have been used in different application areas for the non-invasive assessment of muscle function. The main purpose of our research is to compare the most frequently used algorithms for information extraction from surface EMG signals under varying conditions in terms of the different window lengths, muscle contraction levels, muscles and subjects. In particular we focus on the issue of estimating the slope and intercept to resolve an linear regression line with utilizing real SEMG signals which represents voluntary contractions during thirty seconds.

• Journal of the Korean Society for Precision Engineering
• /
• v.25 no.5
• /
• pp.52-59
• /
• 2008

The accuracy of the machine tools is degraded because of thermal error of structure due to thermal variation. To improve the accuracy of a machine tools, measurement and prediction of thermal error is very important. The main part of thermal source is spindle due to high speed with friction. The thermal error of spindle is very important because it is over 10% in total thermals errors. In this paper, the suitable thermal error prediction technology for machine tools with open architecture controller is developed and implemented to machine tools. Two thermal error prediction technologies, neural network and multi-linear regression, are investigated in several methods. The multi-linear regression method is more effective for implementation to CNC. The developed thermal error prediction technology is implemented on the internal function of CNC.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.5
• /
• pp.1167-1177
• /
• 2017

The censored regression using the pseudo-response variable proposed by Buckley and James has been one of the most well-known models. Recently, the varying coefficient regression model has received a great deal of attention as an important tool for modeling. In this paper we propose a censored varying coefficient regression model using Buckley-James method to consider situations where the regression coefficients of the model are not constant but change as the smoothing variables change. By using the formulation of least squares support vector machine (LS-SVM), the coefficient estimators of the proposed model can be easily obtained from simple linear equations. Furthermore, a generalized cross validation function can be easily derived. In this paper, we evaluated the proposed method and demonstrated the adequacy through simulate data sets and real data sets.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.3
• /
• pp.577-585
• /
• 2009

So far in a nonparametric regression model one of the interesting problems is estimating the error variance. In this paper we propose an estimator of the mean of the squared functions which is the numerator of SNR (Signal to Noise Ratio). To estimate SNR, the mean of the squared function should be firstly estimated. Our focus is on estimating the amplitude, that is the mean of the squared functions, in a nonparametric regression using a simple linear regression model with the quadratic form of observations as the dependent variable and the function of a lag as the regressor. Our method can be extended to nonparametric regression models with multivariate functions on unequally spaced design points or clustered designed points.