• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.77-91
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• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

• Communications for Statistical Applications and Methods
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• 제26권2호
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• pp.149-161
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• 2019

In this article, we suggest the following approaches to simultaneous variable selection and outlier detection. First, we determine possible candidates for outliers using properties of an intercept estimator in a difference-based regression model, and the information of outliers is reflected in the multiple regression model adding mean shift parameters. Second, we select the best model from the model including the outlier candidates as predictors using stochastic search variable selection. Finally, we evaluate our method using simulations and real data analysis to yield promising results. In addition, we need to develop our method to make robust estimates. We will also to the nonparametric regression model for simultaneous outlier detection and variable selection.

• 한국측량학회지
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• 제32권3호
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• pp.225-231
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• 2014

When a spatial regression model that uses kernel density values as a dependent variable is applied to retail business data, a unique model cannot be selected because kernel density values change following kernel bandwidths. To overcome this problem, this paper suggests how to use the point pattern analysis, especially the L-index to select a unique spatial regression model. In this study, kernel density values of retail business are computed by the bandwidth, the distance of the maximum L-index and used as the dependent variable of spatial regression model. To test this procedure, we apply it to meeting room business data in Seoul, Korea. As a result, a spatial error model (SEM) is selected between two popular spatial regression models, a spatial lag model and a spatial error model. Also, a unique SEM based on the real distribution of retail business is selected. We confirm that there is a trade-off between the goodness of fit of the SEM and the real distribution of meeting room business over the bandwidth of maximum L-index.

• Asian-Australasian Journal of Animal Sciences
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• 제21권7호
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• pp.923-927
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• 2008

The objective of this study was to compare random regression model and multiple trait animal model estimates of the (co) variance of total sperm cells over the active lifetime of AI boars. Data were provided by Smithfield Premium Genetics (Rose Hill, NC). Total number of records and animals for the random regression model were 19,629 and 1,736, respectively. Data for multiple trait animal model analyses were edited to include only records produced at 9, 12, 15, 18, 21, 24, and 27 months of age. For the multiple trait method estimates of genetic and residual variance for total sperm cells were heterogeneous among age classifications. When comparing multiple trait method to random regression, heritability estimates were similar except for total sperm cells at 24 months of age. The multiple trait method also resulted in higher estimates of heritability of total sperm cells at every age when compared to random regression results. Random regression analysis provided more detail with regard to changes of variance components with age. Random regression methods are the most appropriate to analyze semen traits as they are longitudinal data measured over the lifetime of boars.

• 대한토목학회논문집
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• 제38권6호
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• pp.801-808
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• 2018

선형회귀모형의 학습은 일반적으로 자료의 개수가 설명변수의 개수보다 충분히 크고, 설명변수들 사이에 심각한 다중공선성이 없다는 가정 하에서 안정적으로 이루어진다. 본 연구에서는 이러한 가정이 위배되었을 경우 모형 학습의 어려움을 실제 호우피해자료를 분석함으로써 조명하였고, 이를 해결하기 위해 자료를 통합한 다음 주성분회귀모형 또는 능형회귀모형을 사용할 것을 검토하였다. 모형의 학습에 사용된 자료와 별도의 독립된 자료에서 제안된 모형들의 예측력을 평가하였고, 제안된 방법이 선형회귀모형보다 더 나은 예측력을 보이는 것을 확인하였다.

• 운동영양학회지
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• 제25권1호
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• pp.35-41
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• 2021

[Purpose] This pilot study aimed to develop a regression model to estimate the excess post-exercise oxygen consumption (EPOC) of Korean adults using various easy-to-measure dependent variables. [Methods] The EPOC and dependent variables for its estimation (e.g., sex, age, height, weight, body mass index, fat-free mass [FFM], fat mass, % body fat, and heart rate_sum [HR_sum]) were measured in 75 healthy adults (31 males, 44 females). Statistical analysis was performed to develop an EPOC estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and HR_sum were important variables in the EPOC regression models of various exercise types. The explanatory power and standard errors of estimates (SEE) for EPOC of each exercise type were as follows: the continuous exercise (CEx) regression model was 86.3% (R²) and 85.9% (adjusted R²), and the mean SEE was 11.73 kcal, interval exercise (IEx) regression model was 83.1% (R²) and 82.6% (adjusted R²), while the mean SEE was 13.68 kcal, and the accumulation of short-duration exercise (AEx) regression models was 91.3% (R²) and 91.0% (adjusted R²), while the mean SEE was 27.71 kcal. There was no significant difference between the measured EPOC using a metabolic gas analyzer and the predicted EPOC for each exercise type. [Conclusion] This pilot study developed a regression model to estimate EPOC in healthy Korean adults. The regression model was as follows: CEx = -37.128 + 1.003 × (FFM) + 0.016 × (HR_sum), IEx = -49.265 + 1.442 × (FFM) + 0.013 × (HR_sum), and AEx = -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum).

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 춘계학술대회 논문집
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• pp.75-80
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• 1999

As current manufacturing processes require high spindle speed and precise machining, increasing accuracy by reducing volumetric errors of the machine itself, particularly thermal errors, is very important. Thermal errors can be estimated by many empirical models, for example, an FEM model, a neural network model, a linear regression model, an engineering judgment model etc. This paper discusses to make a modeling of thermal errors efficiently through backward elimination and fuzzy logic strategy. The model of a thermal error using fuzzy logic strategy overcome limitation of accuracy in the linear regression model or the engineering judgment model. And this model is compared with the engineering judgment model. It is not necessary complex process such like multi-regression analysis of the engineering judgment model. A fuzzy model does not need to know the characteristics of the plant, and the parameters of the model can be mathematically calculated. Like a regression model, this model can be applied to any machine, but it delivers greater accuracy and robustness.

• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• 소성∙가공
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• 제26권6호
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• pp.341-347
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• 2017

Flexibly-reconfigurable roll forming (FRRF) is a novel sheet metal forming technology conducive to produce multi-curvature surfaces by controlling strain distribution along longitudinal direction. Reconfigurable rollers could be arranged to implement a kind of punch die set. By utilizing these reconfigurable rollers, desired curved surface can be formed. In FRRF process, three-dimensional surface is formed from two-dimensional curve. Thus, it is difficult to predict the forming result. In this study, a regression analysis was suggested to construct a predictive model for a longitudinal curvature of FRRF process. To facilitate investigation, input parameters affecting the longitudinal curvature of FRRF were determined as maximum compression value, curvature radius in the transverse direction, and initial blank width. Three-factor three-level full factorial experimental design was utilized and 27 experiments using FRRF apparatus were performed to obtain sample data of the regression model. Regression analysis was carried out using experimental results as sample data. The model used for regression analysis was a quadratic nonlinear regression model. Determination factor and root mean square root error were calculated to confirm the conformity of this model. Through goodness of fit test, this regression predictive model was verified.

• 대한기계학회논문집A
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• 제24권10호
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• pp.2589-2596
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• 2000

As current manufacturing processes require high spindle speed and precise machining, increasing accuracy by reducing volumetric errors of the machine itself, particularly thermal errors, is very important. Thermal errors can be estimated by many empirical models, for example, an FEM model, a neural network model, a linear regression model, an engineering judgment model, etc. This paper discusses to make a modeling of thermal errors efficiently through backward elimination and fuzzy logic strategy. The model of a thermal error using fuzzy logic strategy overcomes limitation of accuracy in the linear regression model or the engineering judgment model. It shows that the fuzzy model has more better performance than linear regression model, though it has less number of thermal variables than the other. The fuzzy model does not need to have complex procedure such like multi-regression and to know the characteristics of the plant, and the parameters of the model can be mathematically calculated. Also, the fuzzy model can be applied to any machine, but it delivers greater accuracy and robustness.