• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.697-710
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• 2021

Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.

• Journal of Sasang Constitutional Medicine
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• v.34 no.1
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• pp.1-12
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• 2022

Objectives This study aimed to examine the differences in the distribution of metabolic syndrome(MetS) and cold hypersensitivity in the hands and feet(CHHF) according to Sasang constitution, and to determine whether CHHF and MetS have an inverse association. Methods MetS and its components, CHHF, Sasang constitution data from 1,998 participants in the Korean medicine Daejeon Citizen Cohort study(KDCC) were obtained. The participants were divided into a non-CHHF(n = 1,270, 63.6%), intermediate(n = 220, 11.0%) and CHHF(n = 508, 25.4%) group according to the thermal sensitivity questionnaire. Sasang constitution was diagnosed by Korea Sasang Constitutional Diagnostic Questionnaire(KS-15). One-way ANOVA and the chi-square test were used for participants' general characteristics and thermal sensitivity and MetS related factors. ANCOVA and logistic regression were used to compare the differences and the odds ratios(ORs) for MetS and its components. Results The MetS and CHHF prevalence rates of the Taeeumin, Soeumin, and Soyangin were 27.6%, 3.8%, 7.7%, and 18.3%, 42.3%, 26.4% respectively. The ANCOVA for MetS components showed that the waist circumference was significantly lower in the CHHF group as compared to the non-CHHF group in total and Soyangin. The logistic regression for MetS prevalence showed that CHHF had a significant inverse association in total(OR = 0.611) and Taeeumin(OR = 0.521). Conclusions The MetS prevalence had the highest in Taeeumin, followed by Soyangin and Soeumin, while the prevalence of CHHF was highest in Soeumin, followed by Soyangin and Taeeumin. In addition, it was confirmed that CHHF and MetS had an inverse association independently.

• Advances in Computational Design
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• v.5 no.3
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• pp.291-304
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• 2020

In this paper, we develop models to design the airfoil using Multilayer Feed-forward Artificial Neural Network (MFANN) and Support Vector Regression model (SVR). The aerodynamic coefficients corresponding to series of airfoil are stored in a database along with the airfoil coordinates. A neural network is created with aerodynamic coefficient as input to produce the airfoil coordinates as output. The performance of the models have been evaluated. The results show that the SVR model yields the lowest prediction error.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.977-985
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• 2010

In this paper, we propose an approach to conduct partial sufficient dimension reduction with heavily unbalanced categorical predictors. For this, we consider integrated categorical predictors and investigate certain conditions that the integrated categorical predictor is fully informative to partial sufficient dimension reduction. For illustration, the proposed approach is implemented on optimal partial sliced inverse regression in simulation and data analysis.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.206.1-206.1
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• 2005

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.393-401
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• 2000

Many graphical methods have been suggested for obtaining an impression of a curvature in regression problem in which some covariates enter nonlinearly. However when true model does not belong to the class of additive models, graphical methods may contain a serious bias. A method is suggested which can avoid such bias in the fitting of nonaddive models.

• Journal of the Korean Graphic Arts Communication Society
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• v.26 no.1
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• pp.51-64
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• 2008

In this paper, an experiment was done where the input(scanner, digital still camera) and monitor(CRT, LCD) device used the linear multiple regression and the GOG (Gain-Offset-Gamma) characterization model to perform a color transformation. Also to color conversion method of the digital printer it used the LUT(Look Up Table), 3dimension linear interpolation and a tetrahedron interpolation method. The results are as follows. From color reappearance of digital printing case of monitor, the XYZ which it converts in linear multiple regression of input device it multiplied the inverse matrix, and then it applies the inverse GOG model and after color converting the patch of the result most which showed color difference below 5 at monitor RGB value. Also, The XYZ which is transmitted from the case input device which is a printer it makes at LAB value to convert an extreme, when the LAB value which is converted calculating the CMY with the LUT and tetrahedral interpolations the color conversion which considers the black quantity was more accurate.

• Journal of Environmental Science International
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• v.27 no.7
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• pp.509-518
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• 2018

Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.6
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• pp.2923-2933
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• 2016

Recently, we reported an inverse association between high 'mate' intake (infusion of Ilex paraguariensis herb, a staple beverage in temperate South America) and breast cancer (BC) risk. Stronger inverse associations were found in high strata of tea, vegetable, fruit and energy intakes, and in overweight/obese women, suggesting possible roles for 'mate' mainly from its antioxidant contribution. The present study attempted to thoroughly explore possible associations among 'mate' and tea intake, dietary antioxidants and BC risk. Combining two databases of previous studies, 572 BC incident cases and 889 controls were interviewed with a specific questionnaire featuring socio-demographic, reproductive and lifestyle variables, and a food frequency questionnaire (64 items), focusing on 'mate' intake (consumer status, daily intake, age at start, age at quit, duration of habit). Food-derived nutrients were calculated from available databases. Odds ratios (OR) and their 95% confidence intervals were calculated through unconditional logistic regression, adjusting for relevant potential confounders. The highest 'mate' intake was significantly inversely associated with BC risk for both low and high carotenoids (OR=0.40 vs. 0.41), vitamin C (OR=0.33 vs. 0.50), vitamin E (OR=0.37 vs. 0.45), flavonols (OR=0.38 vs. 0.48) and reduced glutathione (OR=0.48 vs. 0.46) strata. High tea intake showed significant inverse risk associations only with high carotenoids (OR=0.41), vitamin E (OR=0.48) and reduced glutathione (OR=0.43) strata. In conclusion, a strong and inverse association for 'mate' intake and BC was found, independent of dietary antioxidant levels. Also strong inverse associations with tea intake were more evident only at high levels of certain dietary antioxidants.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.177-185
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• 2014

Under bivariate normal distribution assumptions, the interaction and quadratic terms are needed in the logistic regression model with two predictors. However, depending on the correlation coefficient and the variances of two conditional distributions, the interaction and quadratic terms may not be necessary. Although the need for these terms can be determined by comparing the two scatter plots, it is not as useful for interaction terms. We explore the structure and usefulness of the 3-D residual plot as a tool for dealing with interaction in logistic regression models. If predictors have an interaction effect, a 3-D residual plot can show the effect. This is illustrated by simulated and real data.