• 응용통계연구
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• 제24권5호
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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.

• Communications for Statistical Applications and Methods
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• 제19권2호
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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.

• Transactions on Electrical and Electronic Materials
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• 제6권6호
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• pp.256-261
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• 2005

This paper presents that the modeling to predict the characteristics with respect to the performance of solenoid embedded inductors manufactured by LTCC process via the nonlinear regression model based on the quasi-Newton method. In order to reduce the runs, the design of experiments (DOE) was used to generate the design space. The nonlinear process models were constructed by the piecewise regression model based on the quasi-Newton method for estimating the model coefficient with the break point on the statistical confidence intervals. Those models were verified by the model accuracy checking based on the assumption statistically.

• 한국응용곤충학회지
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• 제26권1호
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• pp.31-36
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• 1987

식물병(植物病) 진전곡선(進展曲線)을 간편하고 융통성있게 기술하는 절편(切片) 1차(次) 회귀(回歸)모델이 본(本) 연구(硏究)에서 제안(提案)되었다. 이 모델은 병진전상황(病進展狀況)을 그 진전형태(進展形態)에 따라 소수(少數)의 1차(次) 회귀식(回歸式)으로 나누고 지표변수(指標變數)를 사용(使用)하여 다시 한개로 묶어 작성(作成)된다. 포장시험(圃場試驗)에서 얻은 12개(個)의 실제병진전상황(實際病進展狀況)에 대(對)한 절편(切片) 1차(次) 회귀(回歸)모델의 통계적(統計的) 적합도(適合度)는 기존(旣存)의 두모델(Logistic모델과 Gompertz모델)에 비(比)하여 증진(增進)되었으며 이 모델이 가진 단순성(單純性), 융통성 및 모수예측(母數豫測)의 용이성(容易性)이 논의(論議)되였다. 그 결과(結果), 절편(切片) 1차(次) 회귀(回歸)모델은 식물병(植物病) 진전(進展)을 기술(記述)하는 한 통계적(統計的) 모델로써 유용(有用)하게 사용(使用)될 수 있으리라 생각된다.

• Communications for Statistical Applications and Methods
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• 제26권6호
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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 the Korean Data and Information Science Society
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• 제10권2호
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• pp.445-455
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• 1999

We modify the Gompertz regression model for estimation of cure rates from pediatric clinical trials by assuming different hazard rates on the different periods. A treatment period may be divided by the stages of treatments under the different treatment arms. The piecewise Gompertz models provide an efficient method for estimation of the cure rates and a method for testing the difference of the treatment effects in the given interval.

• Journal of Welding and Joining
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• 제27권5호
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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.

• Journal of Electrical Engineering and Technology
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• 제11권4호
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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.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권11호
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• pp.632-640
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• 2017

일사량은 태양광 발전시스템의 전력 생산량에 가장 큰 영향을 미치는 기상요소이며, 다른 기상요소들과 달리 기상청의 일기예보를 통해 제공받을 수 없다. 따라서 효율적인 태양광 발전시스템 운용을 위해 일사량 예측에 관한 연구는 필수적이다. 본 연구는 기상정보 데이터 기반의 Dynamic Piecewise 일사량 예측 모델을 제안한다. Dynamic Piecewise 일사량 예측 모델은 유사한 태양고도와 유사한 날씨의 데이터 조각들로 나누어 학습하기 위해, 예측하는 시점의 태양고도와 운량을 기준으로 전체 데이터를 동적으로 나눈 후 기계학습 알고리즘인 다중 선형회귀 알고리즘으로 학습하여 일사량을 예측하는데 사용된다. 본 연구의 성능을 검증하기 위해 제안 모델인 Dynamic Piecewise 일사량 예측 모델과 이전 연구에서 제안한 모델, 기존의 상관관계식 기반 일사량 예측 모델에 동일한 기상정보 데이터 셋을 적용하여 비교하였으며, 비교결과 본 연구에서 제안한 모델이 가장 정확한 일사량 예측 성능을 보였다.

• 한국조경학회지
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• 제33권2호
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• pp.71-82
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• 2005

A large scale of riverside rearrangement has been recently done in the major rivers in Korea. So inundation possibility in agricultural area closed by these rivers has been higher than the possibility a few years ago. However, land use in this area has not been adjusted to a change of this situation near the rivers. Therefore, when typhoon or heavy rain is happened on this area, it can cause a large damage in agricultural area. This study analyzed inundation potentiality in agricultural area at Ibang-myeon, Changnyeong-gun, Kyeongnam-province, Korea by using the logistic regression model and the piecewise regression model. The first thing we did was to transfer the inundation area per elevation to the accumulated inundation area per elevation. This accumulated inundation area per elevation as an distribution function could be described by the logistic regression model(LRM), and piecewise regression model(PRM) could make it much more accurate to analyze the inundation area per elevation. As a result, the regression models derived from LRM and PRM showed $R^2$ over 0.950. The models derived from LRM and PRM in Ibang-myeon noted that frequently inundated area(FIA) was shown up to 12.12m in elevation, and potentially inundated area(PIA) was shown up to 14.60m in elevation. In FIA, regular agricultural activity would be impossible. And It would be not easy to continue the regular agricultural activity in PIA. So, this land should be rearranged to be used for a buffer zone for ecosystem protection, landscape conservation and things like that in riverside.