• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.759-768
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• 2011

Tree algorithms have been widely developed for regression problems. One of the good features of a regression tree is the flexibility of fitting because it can correctly capture the nonlinearity of data well. Especially, data with sudden structural breaks such as the price of oil and exchange rates could be fitted well with a simple mixture of a few piecewise linear regression models. Now that split points are determined by chi-squared statistics related with residuals from fitting piecewise linear models and the split variable is chosen by an objective criterion, we can get a quite reasonable fitting result which goes in line with the visual interpretation of data. The piecewise linear regression by a regression tree can be used as a good fitting method, and can be applied to a dataset with much fluctuation.

• Korean journal of applied entomology
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• v.26 no.1 s.70
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• pp.31-36
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• 1987

A piecewise linear regression model able to describe disease progress curves with simplicity and flexibility was developed in this study. The model divides whole epidemic into several pieces of simple linear regression based on changes in pattern of disease progress in the epidemic and then incorporates the pieces of linear regression into a single mathematical function using indicator variables. When twelve epidemic data obtained from the field experiments were fitted to the piecewise linear regression model, logistic model and Gompertz model to compare statistical fit, goodness of fit was greatly improved with piecewise linear regression compared to other two models. Simplicity, flexibility, accuracy and ease in parameter estimation of the piece-wise linear regression model were described with examples of real epidemic data. The result in this study suggests that piecewise linear regression model is an useful technique for modeling plant disease epidemic.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.539-556
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• 2019

This paper derive a method to solve change point regression problems via a process for obtaining consequential results using properties of a difference-based intercept estimator first introduced by Park and Kim (Communications in Statistics - Theory Methods, 2019) for outlier detection in multiple linear regression models. We describe the statistical properties of the difference-based regression model in a piecewise simple linear regression model and then propose an efficient algorithm for change point detection. We illustrate the merits of our proposed method in the light of comparison with several existing methods under simulation studies and real data analysis. This methodology is quite valuable, "no matter what regression lines" and "no matter what the number of change points".

• Journal of Welding and Joining
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• v.27 no.5
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• pp.42-48
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• 2009

Pipe welding is used in various ranges such as civil engineering and ship building engineering. Until now, many technicians work for pipe welding manually under harmful, dangerous and difficult conditions. So it is necessary to install automation process. For automation pipe welding, relation between welding parameters & bead shape should be considered. Using this relation, bead shape could be expected from welding parameters. FCAW was used in this study. Instead of pipe workpiece, fillet joint plate is used, which were inclined 0,45,90,135,180 degree. By analyzing between welding parameters (current, welding speed, voltage) and bead shape parameters with non-linear multiple regression, bead shape parameters could be expected. Piecewise Cubic Hermite Interpolation was used to expect smooth curved bead shape with bead shape parameters. From these processes, bead shape could be expected from welding parameters.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.293-301
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• 2012

Quantile regression proposed by Koenker and Bassett (1978) is a statistical technique that estimates conditional quantiles. The advantage of using quantile regression is the robustness in response to large outliers compared to ordinary least squares(OLS) regression. A regression tree approach has been applied to OLS problems to fit flexible models. Loh (2002) proposed the GUIDE algorithm that has a negligible selection bias and relatively low computational cost. Quantile regression can be regarded as an analogue of OLS, therefore it can also be applied to GUIDE regression tree method. Chaudhuri and Loh (2002) proposed a nonparametric quantile regression method that blends key features of piecewise polynomial quantile regression and tree-structured regression based on adaptive recursive partitioning. Lee and Lee (2006) investigated wage determinants in the Korean labor market using the Korean Labor and Income Panel Study(KLIPS). Following Lee and Lee, we fit three kinds of quantile regression tree models to KLIPS data with respect to the quantiles, 0.05, 0.2, 0.5, 0.8, and 0.95. Among the three models, multiple linear piecewise quantile regression model forms the shortest tree structure, while the piecewise constant quantile regression model has a deeper tree structure with more terminal nodes in general. Age, gender, marriage status, and education seem to be the determinants of the wage level throughout the quantiles; in addition, education experience appears as the important determinant of the wage level in the highly paid group.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.974-986
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• 2016

Reversible image watermarking, a type of digital data hiding, is capable of recovering the original image and extracting the hidden message with precision. A number of reversible algorithms have been proposed to achieve a high embedding capacity and a low distortion. While numerous algorithms for the achievement of a favorable performance regarding a small embedding capacity exist, the main goal of this paper is the achievement of a more favorable performance regarding a larger embedding capacity and a lower distortion. This paper therefore proposes a reversible data hiding algorithm for which a novel piecewise 2D auto-regression (P2AR) predictor that is based on a rhombus-embedding scheme is used. In addition, a minimum description length (MDL) approach is applied to remove the outlier pixels from a training set so that the effect of a multiple linear regression can be maximized. The experiment results demonstrate that the performance of the proposed method is superior to those of previous methods.

• KIISE Transactions on Computing Practices
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• v.23 no.11
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• pp.632-640
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• 2017

Although solar insolation is the weather factor with the greatest influence on power generation in photovoltaic systems, the Meterological Agency does not provide solar insolation data for future dates. Therefore, it is essential to research prediction methods for solar insolation to efficiently manage photovoltaic systems. In this study, we propose a Dynamic Piecewise Prediction Model that can be used to predict solar insolation values for future dates based on information from the weather forecast. To improve the predictive accuracy, we dynamically divide the entire data set based on the sun altitude and cloudiness at the time of prediction. The Dynamic Piecewise Prediction Model is developed by applying a polynomial linear regression algorithm on the divided data set. To verify the performance of our proposed model, we compared our model to previous approaches. The result of the comparison shows that the proposed model is superior to previous approaches in that it produces a lower prediction error.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.139-148
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• 2012

PURPOSES : The accuracy of travel time information is a key measure of effectiveness and reliability of advanced traveler information systems. This study aims at investigating drivers' perception on the acceptable level of information accuracy and their corresponding valuations. METHODS : A questionnaire survey was executed for collecting driver perception data to capture the expectation level of travel time information provided and their willingness to pay for the information. A Tobit model was adopted for exploring the relationship among the acceptable level, driver socioeconomic characteristics and travel attributes. Since drivers' willingness to pay for accurate travel time information can be different according to their travel lengths, a piecewise linear regression model was developed to capture the sensitivity of values of travel time information to travel length. RESULTS : The analysis results suggest that trip purpose and travel time are two dominant factors to determine drivers' acceptable level of travel time information. For business and short trips, drivers want more accurate information than for non-business and long trips. Drivers' willingness to pay for travel time information also varies depending on their incomes, trip purposes and travel lengths. The results also show that drivers' valuation of travel time information provided is sensitive to their travel length. For longer trips, drivers are less sensitive to travel time information and then put less value on the information provided. CONCLUSIONS : Censored nonlinear regression models are developed to estimate drivers' acceptable accuracy for travel time information and their valuation using questionnaire survey data. The findings on drivers perception to the required accuracy of travel time information and their corresponding willingness to pay can be used in the design and deployment of advanced traveler information system to improve its effectiveness and usefulness through high compliance.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.1
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• pp.39-51
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• 1994

Almost all accelerated life tests assume that no basic failure mechanism changes within the test stresses. But accelerated life test, considering failure mechanism changes, is needed since failure mechanism changes when accelerating beyond the used stress. This paper studies the analysis when the failure mechanism changes within the test stresses. The piecewise linear regression, which the join point of two lines is estimated, is applied In particular, two accelerated life tests, with and without a change in failure mechanism are examined.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.106-109
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• 1998

Wireless communication systems, in particular, must operate in a crowded electro-magnetic environmnet where in-band undesired signals are treated as noise by the receiver. These interfering signals are often random but not Gaussian Due to nongaussian noise, the distribution of the observables cannot be specified by a finite set of parameters; instead r-dimensioal sample space (pure noise samples) is equiprobably partitioned into a finite number of disjointed regions using quantiles and a vector quantizer based on training samples. If we assume that the detected symbols are correct, then we can observe the pure noise samples during the training and transmitting mode. The algorithm proposed is based on a piecewise approximation to a regression function based on quantities and conditional partition moments which are estimated by a RMSA (Robbins-Monro Stochastic Approximation) algorithm. In this paper, we develop a diversity combiner with modified detector, called Non-Linear Detector, and the receiver has a differential phase detector in each diversity branch and at the combiner each detector output is proportional to the second power of the envelope of branches. Monte-Carlo simulations were used as means of generating the system performance.