Assessing robustness of designs for random effects parameters for nonlinear mixed-effects models

2017 ◽  
Vol 44 (6) ◽  
pp. 611-616 ◽  
Author(s):  
Stephen B. Duffull ◽  
Andrew C. Hooker
Keyword(s):  
Random Effects ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Nonlinear Mixed Effects ◽  
Nonlinear Mixed Effects Models

Fitting Growth Model Using Nonlinear Regression with Random Parameters

2011 ◽  
Vol 480-481 ◽  
pp. 1308-1312
Author(s):  
Yao Xiang Li ◽  
Li Chun Jiang
Keyword(s):  
Random Effects ◽  
Repeated Measures ◽  
Fixed Effects ◽  
Model Building ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Ratio Test ◽  
Nonlinear Mixed Effects ◽  
Nonlinear Mixed Effects Models ◽  
Mixed Effect

Mixed Effect models are flexible models to analyze grouped data including longitudinal data, repeated measures data, and multivariate multilevel data. One of the most common applications is nonlinear growth data. The Chapman-Richards model was fitted using nonlinear mixed-effects modeling approach. Nonlinear mixed-effects models involve both fixed effects and random effects. The process of model building for nonlinear mixed-effects models is to determine which parameters should be random effects and which should be purely fixed effects, as well as procedures for determining random effects variance-covariance matrices (e.g. diagonal matrices) to reduce the number of the parameters in the model. Information criterion statistics (AIC, BIC and Likelihood ratio test) are used for comparing different structures of the random effects components. These methods are illustrated using the nonlinear mixed-effects methods in S-Plus software.

Calculating empirical best linear unbiased predictors (EBLUPs) for nonlinear mixed effects models in Excel/Solver

2012 ◽  
Vol 88 (03) ◽  
pp. 340-344 ◽  
Author(s):  
Gordon Nigh
Keyword(s):  
Random Effects ◽  
Conditional Expectation ◽  
Taylor Series Expansion ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Iterative Technique ◽  
Nonlinear Mixed Effects ◽  
Nonlinear Mixed Effects Models ◽  
Best Linear Unbiased ◽  
Excel Solver

Nonlinear mixed-effects models have become common in the forestry literature. Calibration of these models for a new subject (one not used in the fitting of the model) involves estimating the values of the of random-effects parameters. Estimators can be obtained by taking a Taylor-series expansion of the nonlinear model around the expected value or the conditional expectation of the random-effects parameters. The conditional expectation method requires an iterative technique to find the estimates, which can be a complicated programming exercise. This note describes a relatively easy way to do the calculations necessary for both the zero expansion and conditional expectation methods in Excel and demonstrates the procedure on a small example.

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