• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.239-250
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• 2022

In many applications, we frequently encounter correlated multiple outcomes measured on the same subject. Joint modeling of such multiple outcomes can improve efficiency of inference compared to independent modeling. For instance, in developmental toxicity studies, fetal weight and number of malformed pups are measured on the pregnant dams exposed to different levels of a toxic substance, in which the association between such outcomes should be taken into account in the model. The number of malformations may possibly have many zeros, which should be analyzed via zero-inflated count models. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint modeling framework for continuous and count outcomes with excess zeros. In our model, zero-inflated Poisson (ZIP) regression model would be used to describe count data, and a subject-specific random effects would account for the correlation across the two outcomes. We implement a Bayesian approach using MCMC procedure with data augmentation method and adaptive rejection sampling. We apply our proposed model to dose-response analysis in a developmental toxicity study to estimate the benchmark dose in a risk assessment.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.7
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• pp.914-918
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• 2004

The random regression model methodology was applied into the estimation of genetic parameters for body weights in Chinese Simmental cattle to replace the traditional multiple trait models. The variance components were estimated using Gibbs sampling procedure on Bayesion theory. The data were extracted for Chinese Simmental cattle born during 1980 to 2000 from 6 national breeding farms, where records from 3 months to 36 months were only used in this study. A 3 orders Legendre polynomial was defined as the submodel to describe the general law of that body weight changing with months of age in population. The heritabilities of body weights from 3 months to 36 months varied between 0.31 and 0.48, where the heritabilities from 3 months to 12 months slightly decreased with months of age but ones from 13 months to 36 months increased with months of age. Specially, the heritabilities at eighteenth and twenty-fourth month of age were 0.33 and 0.36, respectively, which were slightly greater than 0.30 and 0.31 from multiple trait models. In addition, the genetic and phenotypic correlations between body weights at different month ages were also obtained using regression model.

• Water for future
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• v.17 no.2
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• pp.107-112
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• 1984

It si presented here that in order to estimate reservoir sedimentation rate through the use of reservoir survey data of 66 irrigation reservoir in 3 major watersheds in this country, the correlation between reservoir sedimentation rate and the following factors; watershed area, trap-efficiency, watershed slope, shape factor of water shed, and reservoir deposition age in two models simple regression model and multiple regression model. Appropriatness of the proposed models have been calibrated from the survey data and as a result, it has been determined that the multiple regression model is much more accurate than the simple regression model. The annual sediment yield is correlated with watershed area and reservoir trap efficiency. It has been found that variation of the annual average sedimentation rate and the annual reservoir capacity loss rate are influenced by the trap efficiency of reservoir.

• Computers and Concrete
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• v.26 no.2
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• pp.175-183
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• 2020

Fly ash has become an important component of concrete as supplementary cementitious material with the development of concrete technology. To make use of fly ash efficiently, four types of fly ash with particle size distributions that are in conformity with four functions, namely, S.Tsivilis, Andersen, Normal and F distribution, respectively, were prepared. The four particle size distributions as functions of the strength and pore structure of concrete were thereafter constructed and investigated. The results showed that the compressive and flexural strength of concrete with the fly ash that conforming to S.Tsivilis, Normal, F distribution increased by 5-10 MPa and 1-2 MPa, respectively, compared to the reference sample at 28 d. The pore structure of the concrete was improved, in which the total porosity of concrete decreased by 2-5% at 28 d. With regarding to the fly ash with Andersen distribution, it was however not conducive to the strength development of concrete. Regression model based on the grey multiple linear regression theory was proved to be efficient to predict the strength of concrete, according to the characteristic parameters of particle size and pore structure of the fly ash.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.137-145
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• 2020

Various elements of Fabrication (FAB), mass production of existing products, new product development and process improvement evaluation might increase the complexity of production process when products are produced at the same time. As a result, complex production operation makes it difficult to predict production capacity of facilities. In this environment, production forecasting is the basic information used for production plan, preventive maintenance, yield management, and new product development. In this paper, we tried to develop a multiple linear regression analysis model in order to improve the existing production capacity forecasting method, which is to estimate production capacity by using a simple trend analysis during short time periods. Specifically, we defined overall equipment effectiveness of facility as a performance measure to represent production capacity. Then, we considered the production capacities of interrelated facilities in the FAB production process during past several weeks as independent regression variables in order to reflect the impact of facility maintenance cycles and production sequences. By applying variable selection methods and selecting only some significant variables, we developed a multiple linear regression forecasting model. Through a numerical experiment, we showed the superiority of the proposed method by obtaining the mean residual error of 3.98%, and improving the previous one by 7.9%.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.269-282
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• 2017

In this study, the surface broadband emissivity ($3.0-14.0{\mu}m$) was calculated using the multiple linear regression model with narrow bands (channels 29, 30, and 31) emissivity data of the Moderate Resolution Imaging Spectroradiometer (MODIS) on Earth Observing System Terra satellite. The 307 types of spectral emissivity data (123 soil types, 32 vegetation types, 19 types of water bodies, 43 manmade materials, and 90 rock) with MODIS University of California Santa Barbara emissivity library and Advanced Spaceborne Thermal Emission & Reflection Radiometer spectral library were used as the spectral emissivity data for the derivation and verification of the multiple linear regression model. The derived determination coefficient ($R^2$) of multiple linear regression model had a high value of 0.95 (p<0.001) and the root mean square error between these model calculated and theoretical broadband emissivities was 0.0070. The surface broadband emissivity from our multiple linear regression model was comparable with that by Wang et al. (2005). The root mean square error between surface broadband emissivities calculated by models in this study and by Wang et al. (2005) during January was 0.0054 in Asia, Africa, and Oceania regions. The minimum and maximum differences of surface broadband emissivities between two model results were 0.0027 and 0.0067 respectively. The similar statistical results were also derived for August. The surface broadband emissivities by our multiple linear regression model could thus be acceptable. However, the various regression models according to different land covers need be applied for the more accurate calculation of the surface broadband emissivities.

• Bulletin of the Korean Chemical Society
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• v.22 no.1
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• pp.37-42
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• 2001

Ridge regression is compared with multiple linear regression (MLR) for determination of Research Octane Number (RON) when the baseline and signal-to-noise ratio are varied. MLR analysis of near-infrared (NIR) spectroscopic data usually encounters a collinearity problem, which adversely affects long-term prediction performance. The collinearity problem can be eliminated or greatly improved by using ridge regression, which is a biased estimation method. To evaluate the robustness of each calibration, the calibration models developed by both calibration methods were used to predict RONs of gasoline spectra in which the baseline and signal-to-noise ratio were varied. The prediction results of a ridge calibration model showed more stable prediction performance as compared to that of MLR, especially when the spectral baselines were varied. . In conclusion, ridge regression is shown to be a viable method for calibration of RON with the NIR data when only a few wavelengths are available such as hand-carry device using a few diodes.

• Journal of the Korean Society of Safety
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• v.30 no.1
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• pp.8-13
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• 2015

The experimental design methodology was applied in the drop tube furnace (DTF) to predict the various combustion properties according to the operating conditions and to assess the coal plant safety. Response surface method (RSM) was introduced as a design of experiment, and the database for RSM was set with the numerical simulation of DTF. The dependent variables such as burnout ratios (BOR) of coal and $CO/CO_2$ ratios were mathematically described as a function of three independent variables (coal particle size, carrier gas flow rate, wall temperature) being modeled by the use of the central composite design (CCD), and evaluated using a second-order polynomial multiple regression model. The prediction of BOR showed a high coefficient of determination (R2) value, thus ensuring a satisfactory adjustment of the second-order polynomial multiple regression model with the simulation data. However, $CO/CO_2$ ratio had a big difference between calculated values and predicted values using conventional RSM, which might be mainly due to the dependent variable increses or decrease very steeply, and hence the second order polynomial cannot follow the rates. To relax the increasing rate of dependent variable, $CO/CO_2$ ratio was taken as common logarithms and worked again with RSM. The application of logarithms in the transformation of dependent variables showed that the accuracy was highly enhanced and predicted the simulation data well.

• Food Science and Biotechnology
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• v.14 no.5
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• pp.656-660
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• 2005

The relationship between the gas chromatographic (GC) patterns of sauce made of barley bran and ranked order in sensory analysis was investigated by multiple regression analysis (MRA). Most of the 42 barley bran sauce samples comprised about 34 peaks, in which the content of 9, 12-octadecanoic acid methyl ester was the highest, followed by those of 2-furanmethanol and 2-furancarboxaldehyde. It is difficult to estimate the aroma quality of barley bran sauce samples on the basis of only one peak. The 34 aroma compounds of the 42 samples were analyzed by an MRA model featuring six transformations. The most precise fit was calculated from the absolute value transformed with the root square of each peak, and the multiple determination coefficient showed that 91.6% of the variation in the sensory score could be explained on the basis of GC data.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.64-71
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• 2013

Purpose: The purpose of this study was to select standing posture parameters that have a significant difference according to the severity of spinal deformity, and to develop a novel Cobb angle prediction model for adolescent girls with idiopathic scoliosis. Methods: Five normal adolescents girls with no history of musculoskeletal disorders, 13 mild scoliosis patients (Cobb angle: $10^{\circ}-25^{\circ}$), and 14 severe scoliosis patients (Cobb angle: $25^{\circ}-50^{\circ}$) participated in this study. Six infrared cameras (VICON) were used to acquire data and 35 standing parameters of scoliosis patients were extracted from previous studies. Using the ANOVA and post-hoc test, parameters that had significant differences were extracted. In addition, these standing posture parameters were utilized to develop a Cobb-angle prediction model through multiple regression analysis. Results: Twenty two of the parameters showed differences between at least two of the three groups and these parameters were used to develop the multi-linear regression model. This model showed a good agreement ($R^2$ = 0.92) between the predicted and the measured Cobb angle. Also, a blind study was performed using 5 random datasets that had not been used in the model and the errors were approximately $3.2{\pm}1.8$. Conclusions: In this study, we demonstrated the possibility of clinically predicting the Cobb angle using a non-invasive technique. Also, monitoring changes in patients with a progressive disease, such as scoliosis, will make possible to have determine the appropriate treatment and rehabilitation strategies without the need for radiation exposure.