• The Korean Journal of Applied Statistics
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• v.6 no.2
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• pp.247-252
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• 1993

Consider minimizing the sum of absolute deviations for multiple regression models. If a regression line is assumed to pass a given point, then we can find that the $L_1$ regression coefficient can be defined in terms of the weighted medians of the slopes from each data point to the given point. Therefore, $L_1$ method could be regarded to find the optimal point which regression line passes over.

• Journal of Mechanical Science and Technology
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• v.16 no.3
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• pp.327-337
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• 2002

This paper applies a recently developed on-line parameter identification (PID) technique to sets of real flight data and compares the results with those of a state-of-the-art off-line PID technique. The on-line PID technique takes Linear Regression from Fourier Transformed equations and the off-line PID is based on the traditional Maximum Likelihood method. Sets of flight data from the NASA F/A-18 High Alpha research Vehicle (HARV) circraft, which has been recorded from specifically designed maneuvers and used for our line parameter estimation, are used for this study. The emphasis is given on the accuracy and on-line measure of reliability of the estimates. The comparison is performed for both longitudinal and lateral-directional dynamics for maneuvers at angles of attack ranging u=20°through $\alpha$=40°. Results of the two estimation processes are also compared with baseline wind tunnel estimates whenever possible.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.448-448
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• 2018

In this study, the regression models (Load ESTimator and eight-parameter model) were evaluated to estimate instantaneous pollutant loads under various criteria and optimization methods. As shown in the results, LOADEST commonly used in interpolating pollutant loads could not necessarily provide the best results with the automatic selected regression model. It is inferred that the various regression models in LOADEST need to be considered to find the best solution based on the characteristics of watersheds applied. The recently developed eight-parameter model integrated with Genetic Algorithm (GA) and Gradient Descent Method (GDM) were also compared with LOADEST indicating that the eight-parameter model performed better than LOADEST, but it showed different behaviors in calibration and validation. The eight-parameter model with GDM could reproduce the nitrogen loads properly outside of calibration period (validation). Furthermore, the accuracy and precision of model estimations were evaluated using various criteria (e.g., $R^2$ and gradient and constant of linear regression line). The results showed higher precisions with the $R^2$ values closed to 1.0 in LOADEST and better accuracy with the constants (in linear regression line) closed to 0.0 in the eight-parameter model with GDM. In hence, based on these finding we recommend that users need to evaluate the regression models under various criteria and calibration methods to provide the more accurate and precise results for pollutant load estimations.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.303-309
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• 1995

Many measures to detect multicollinearity in linear regression have been proposed in statistics and numerical analysis literature. Among them, condition number and variance inflation factor(VIF) are most popular. In this study, we give new interpretations of condition number and VIF in linear regression, using geometry on the explanatory space. In the same line, we derive natural measures of condition number and VIF for logistic regression. These computer intensive measures can be easily extended to evaluate multicollinearity in generalized linear models.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.588-590
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• 2006

Recently, many on-line approaches to instrument channel surveillance (drift monitoring and fault detection) have been reported worldwide. On-line monitoring (OLM) method evaluates instrument channel performance by assessing its consistency with other plant indications through parametric or non-parametric models. The heart of an OLM system is the model giving an estimate of the true process parameter value against individual measurements. This model gives process parameter estimate calculated as a function of other plant measurements which can be used to identify small sensor drifts that would require the sensor to be manually calibrated or replaced. This paper describes an improvement of auto-associative kernel regression by introducing a correlation coefficient weighting on kernel distances. The prediction performance of the developed method is compared with conventional auto-associative kernel regression.

• Journal of the Korean Solar Energy Society
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• v.30 no.4
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• pp.15-21
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• 2010

Radiation data are the best source of information for estimating average incident radiation. Lacking this or data from nearby locations of similar climate, it is possible to use empirical relation ships to estimate radiation from days of cloudiness. It is necessary to estimate the regression coefficients in order to predict the daily global radiation on a horizontal surface. There fore many different equations have proposed to evaluate them for certain areas. In this work a new correlation has been made to predict the solar radiation for 16 different areas over Korea by estimating the regression coefficients taking into account cloud cover. Particularly, the straight line regression model proposed shows reliable results for estimating the global radiation on a horizontal surface with monthly average deviation of -0.26 to +0.53% and each station annual average deviation of -1.61 to +1.7% from measured values.

• Journal of the Korean Statistical Society
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• v.5 no.1
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• pp.19-28
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• 1976

In empirical study for fitting a multiple linear regression model for successive cross-sections data observed on the same set of independent variables over several time periods, one often faces the problem of poor $R^2$, the multiple coefficient of determination, which provides a standard measure of how good a specified regression line fits the sample data.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.187-189
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• 2004

This paper investigates the effect of weather factors(such as winds, rain, snows, temperature, clouds and humidity) on transmission line outages. The result shows that weather variables have significant effects on the transmission line historical outages and the relationship between them is nonlinear. Multiple regression analysis using Logit model is proved to be appropriate in forecasting line failure rate in KEPCO systems. It could also provide system operators with useful informations about system operation and planing.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.27-37
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• 2005

Direct writing technology on the silicon wafer surface is used to reduce the size of the chip as the miniature trend in electronic circuit. In order to improve the productivity and efficiency, the real time quality estimation is very important in each semiconductor process. In laser marking, marking quality is determined by readability which is dependant on the contrast of surface, the line width, and the melting depth. Many researchers have tried to find theoretical and numerical estimation models fur groove geometry. However, these models are limited to be applied to the real system. In this study, the estimation system for the line width during the laser marking was proposed by process monitoring method. The light intensity emitted by plasma which is produced when irradiating the laser to the silicon wafer was measured using the optical sensor. Because the laser marking is too fast to measure with external sensor, we build up the coaxial monitoring system. Analysis for the correlation between the acquired signals and the line width according to the change of laser power was carried out. Also, we developed the models enabling the estimation of line width of the laser marking through the statistical regression models and may see that their estimating performances were excellent.

• Journal of Korean Society for Quality Management
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• v.29 no.3
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• pp.124-137
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• 2001

In general, it is very efficient and effective to use screening variables that are correlated with the performance variable in case that measuring the performance variable is impossible (destructive) or expensive. The general methodology for searching surrogate variables is regression analysis. This paper considers the inspection problem in CRT (Cathode Ray Tube) production line, in which the performance variable (dependent variable) is binary type and screening variables are continuous. The general regression with dummy variable, discriminant analysis and binary logistic regression are considered. The cost model is also formulated to determine economically inspection procedure with screening variables.