• Communications for Statistical Applications and Methods
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• 제27권6호
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• pp.701-714
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• 2020

Quantitative high throughput screening (qHTS) assays are used to assess toxicity for many chemicals in a short period by collectively analyzing them at several concentrations. Data are routinely analyzed using nonlinear regression models; however, we propose a new method to analyze qHTS data using a nonlinear mixed effects model. qHTS data are generated by repeating the same experiment several times for each chemical; therefor, they can be viewed as if they are repeated measures data and hence analyzed using a nonlinear mixed effects model which accounts for both intra- and inter-individual variabilities. Furthermore, we apply a one-step approach incorporating robust estimation methods to estimate fixed effect parameters and the variance-covariance structure since outliers or influential observations are not uncommon in qHTS data. The toxicity of chemicals from a qHTS assay is classified based on the significance of a parameter related to the efficacy of the chemicals using the proposed method. We evaluate the performance of the proposed method in terms of power and false discovery rate using simulation studies comparing with one existing method. The proposed method is illustrated using a dataset obtained from the National Toxicology Program.

• Journal of the Korean Data and Information Science Society
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• 제20권2호
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• pp.445-452
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• 2009

Credibility models are actuarial tools to distribute premiums fairly among a heterogeneous group of policyholders. Many existing credibility models can be expressed as special cases of linear mixed effects models. In this paper we propose a nonlinear credibility regression model by reforming the linear mixed effects model through kernel machine. The proposed model can be seen as prediction method applicable in any setting where repeated measures are made for subjects with different risk levels. Experimental results are then presented which indicate the performance of the proposed estimating procedure.

• Computers and Concrete
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• 제4권2호
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• pp.135-156
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• 2007

Three 3D nonlinear finite-element models are developed to study the behavior of concrete beams and plates with and without external reinforcement by fibre-reinforced plastic (FRP). All three models are formulated based upon the 3D theory of elasticity. The stress model is modified from the element developed by Ramtekkar, et al. (2002) to incorporate material nonlinearity in the formulation. Both transverse stress and displacement components are used as nodal degrees-of-freedom to ensure the continuity of both stress and displacement components between the elements. The displacement model uses only displacement components as nodal degrees-of-freedom. The transition model has both stress and displacement components as nodal degrees-of-freedom on one surface, and only displacement components as nodal degrees-of-freedom on the opposite surface. The transition model serves as a connector between the stress and the displacement models. The developed models are validated by comparing the results of the analyses with an existing experimental result. Parametric studies of the effects of the externally reinforced FRP on the load capacity of reinforced concrete (RC) beams and concrete plates are performed to demonstrate the practicality and the efficiency of the proposed models.

• 대한임상약리학회:학술대회논문집
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• 대한임상약리학회 2006년도 제15차 추계학술대회
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• pp.37-38
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• 2006

• 응용통계연구
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• 제31권1호
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• pp.123-137
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• 2018

비선형 혼합효과 모형은 다양한 분야에서 반복 측정 자료를 분석할 때 주로 사용된다. 비선형 혼합효과 모형은 개체 내 변동(intra-individual variation)에 대해 고려하는 제 1단계 개별수준모델(individual-level model)과 개체간 변동(inter-individual variation)에 대해 고려하는 제 2단계 개체군모델(population model)의 두 단계로 구성되어 있다. 비선형 혼합효과 모형의 첫 번째 단계인 개별수준모델은 비선형 회귀모형의 모수를 추정하는 것으로 일반적인 비선형 회귀모형과 같고, 주로 보통최소제곱추정 방법을 사용하여 모수를 추정한다. 그러나 최소제곱추정방법은 가정된 비선형 함수가 자료에 의해 명시적으로 드러나지 않는 경우 모수의 추정값과 그 표준오차가 극단적으로 커지는 문제가 발생할 수 있다. 본 논문에서는 최근에 비선형 회귀모형에서 제안된 능형회귀(ridge regression) 방법을 비선형 혼합효과 모형의 제 1단계 개별수준모델에 도입함으로써 이러한 문제를 해결할 수 있는 새로운 추정방법을 제안하였다. 제안된 추정량은 모의실험 연구를 통하여 기존의 표준적인 추정량과 그 성능을 비교하였다. 또한 미국의 National Toxicology Program으로부터 얻어진 정량적 대량고속 스크리닝(quantitative high throughput screening) 실제 자료를 사용하여 추정 방법들을 비교하였다.

• Animal Bioscience
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• 제35권5호
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• pp.648-658
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• 2022

Objective: The identification of nonlinear mixed models that describe the growth trajectory of New Zealand rabbits was performed based on weight records and carcass measures obtained using ultrasonography. Methods: Phenotypic records of body weight (BW) and loin eye area (LEA) were collected from 66 animals raised in a didactic-productive module of cuniculture located in the southern Piaui state, Brazil. The following nonlinear models were tested considering fixed parameters: Brody, Gompertz, Logistic, Richards, Meloun 1, modified Michaelis-Menten, Santana, and von Bertalanffy. The coefficient of determination (R²), mean squared error, percentage of convergence of each model (%C), mean absolute deviation of residuals, Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to determine the best model. The model that best described the growth trajectory for each trait was also used under the context of mixed models, considering two parameters that admit biological interpretation (A and k) with random effects. Results: The von Bertalanffy model was the best fitting model for BW according to the highest value of R² (0.98) and lowest values of AIC (6,675.30) and BIC (6,691.90). For LEA, the Logistic model was the most appropriate due to the results of R² (0.52), AIC (783.90), and BIC (798.40) obtained using this model. The absolute growth rates estimated using the von Bertalanffy and Logistic models for BW and LEA were 21.51g/d and 3.16 cm², respectively. The relative growth rates at the inflection point were 0.028 for BW (von Bertalanffy) and 0.014 for LEA (Logistic). Conclusion: The von Bertalanffy and Logistic models with random effect at the asymptotic weight are recommended for analysis of ponderal and carcass growth trajectories in New Zealand rabbits. The inclusion of random effects in the asymptotic weight and maturity rate improves the quality of fit in comparison to fixed models.

• 한국산림과학회지
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• 제111권1호
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• pp.136-149
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• 2022

강원지방소나무의 수간곡선 추정에 비선형 혼합효과 모형(nonlinear mixed-effects models: NLME)을 적용하고 몇 가지 성능 척도를 이용하여 비선형 고정효과 모형과 비교하였다. 혼합효과 모형이 고정효과 모형을 개선한 정도를 전체 성능의 측면에서 설명하면, 수간직경에 대해서는 BIAS 26.4%, MAB 42.9%, RMSE 43.1%, FI 0.9%만큼이었고, 수간단면적에 대해서는 BIAS 67.7%, MAB 44.7%, RMSE 45.8%, FI 1.0%만큼이었다. 수간을 12개의 상대수고 구간으로 세분화하여 분석한 결과에서도 수간곡선의 성능은 혼합효과 모형에 의해 크게 개선된 것으로 나타났다. 혼합효과 모형은 모든 상대수고 구간에서 수간직경 및 수간단면적에 대한 성능이 고정효과 모형보다 더 나은 MAB, RMSE, FI를 나타내었고, BIAS의 경우 일부 구간(수간직경: 0.05, 0.2, 0.3, 0.8, 수간단면적: 0.05, 0.3, 0.5, 0.6, 1.0)에서만 고정효과 모형보다 뒤떨어지는 것으로 확인되었다. 특히 지상에 근접한 수간 최하부(수고 0.2 m 지점)에서 수간직경 및 수간단면적 추정 성능이 혼합효과 모형에 의해 크게 향상되었다. 수간직경의 경우 BIAS 84.2%, MAB 69.8%, RMSE 68.7%, FI 3.1%, 수간단면적의 경우 BIAS 98.5%, MAB 70.1%, RMSE 68.7%, FI 3.1%만큼 향상된 것으로 분석되었다. 지상으로부터 0.2 m 높이 지점의 수간단면적이 수간단면적 전체에서 차지하는 비중은 22.7%에 달하였다. 이렇게 수간재적에서 가장 큰 비중을 차지하는 수간 최하부에서의 추정 성능이 크게 향상되었다는 것은 전체 수간재적의 추정 성능 역시 큰 폭으로 향상될 수 있음을 시사한다. 비록 모형 적합 과정이 고정효과 모형보다 까다롭다는 단점이 있지만, 추정 성능의 개선 효과를 고려하면 NLME를 수간곡선 추정의 표준적인 방법으로 사용하는 것을 검토할 필요가 있다.

• Advances in nano research
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• 제6권2호
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• pp.163-182
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• 2018

Based on the Reissner mixed variational theorem (RMVT), the authors present a nonlocal Timoshenko beam theory (TBT) for the nonlinear free vibration analysis of multi-walled carbon nanotubes (MWCNT) embedded in an elastic medium. In this formulation, four different edge conditions of the embedded MWCNT are considered, two different models with regard to the van der Waals interaction between each pair of walls constituting the MWCNT are considered, and the interaction between the MWCNT and its surrounding medium is simulated using the Pasternak-type foundation. The motion equations of an individual wall and the associated boundary conditions are derived using Hamilton's principle, in which the von $K{\acute{a}}rm{\acute{a}}n$ geometrical nonlinearity is considered. Eringen's nonlocal elasticity theory is used to account for the effects of the small length scale. Variations of the lowest frequency parameters with the maximum modal deflection of the embedded MWCNT are obtained using the differential quadrature method in conjunction with a direct iterative approach.

• 한국신뢰성학회지:신뢰성응용연구
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• 제18권1호
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• pp.33-39
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• 2018

Purpose: Accelerated degradation tests can be effective in assessing product reliability when degradation leading to failure can be observed. This article proposes an accelerated degradation test model for highly reliable solid state drives (SSDs). Methods: We suggest a nonlinear mixed-effects (NLME) model to degradation data for SSDs. A Monte Carlo simulation is used to estimate lifetime distribution in accelerated degradation testing data. This simulation is performed by generating random samples from the assumed NLME model. Conclusion: We apply the proposed method to degradation data collected from SSDs. The derived power model is shown to be much better at fitting the degradation data than other existing models. Finally, the Monte Carlo simulation based on the NLME model provides reasonable results in lifetime estimation.

• Asian-Australasian Journal of Animal Sciences
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• 제24권1호
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• pp.17-27
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• 2011

A selection experiment for reduced residual feed intake (RFI) in Yorkshire pigs consisted of a line selected for lower RFI (LRFI) and a random control line (CTRL). Longitudinal measurements of daily feed intake (DFI) and body weight (BW) from generation 5 of this experiment were used. The objectives of this study were to evaluate the use of random regression (RR) and nonlinear mixed models to predict DFI and BW for individual pigs, accounting for the substantial missing information that characterizes these data, and to evaluate the effect of selection for RFI on BW and DFI curves. Forty RR models with different-order polynomials of age as fixed and random effects, and with homogeneous or heterogeneous residual variance by month of age, were fitted for both DFI and BW. Based on predicted residual sum of squares (PRESS) and residual diagnostics, the quadratic polynomial RR model was identified to be best, but with heterogeneous residual variance for DFI and homogeneous residual variance for BW. Compared to the simple quadratic and linear regression models for individual pigs, these RR models decreased PRESS by 1% and 2% for DFI and by 42% and 36% for BW on boars and gilts, respectively. Given the same number of random effects as the polynomial RR models, i.e., two for BW and one for DFI, the non-linear Gompertz model predicted better than the polynomial RR models but not as good as higher order polynomial RR models. After five generations of selection for reduced RFI, the LRFI line had a lower population curve for DFI and BW than the CTRL line, especially towards the end of the growth period.