scholarly journals Testing the Correlation and Heterogeneity for Hierarchical Nonlinear Mixed-Effects Models

2011 ◽  
Vol 2011 ◽  
pp. 1-16
Author(s):  
Qingming Zou ◽  
Zhongyi Zhu
Keyword(s):  
Repeated Measures ◽  
Asymptotic Properties ◽  
Mixed Effects ◽  
Common Interest ◽  
Mixed Effects Models ◽  
Nonlinear Mixed Effects ◽  
Finite Sample ◽  
Nonlinear Mixed Effects Models ◽  
Nonlinear Mixed Effects Model ◽  
Repeated Measures Data

Nonlinear mixed-effects models are very useful in analyzing repeated-measures data and have received a lot of attention in the field. It is of common interest to test for the correlation within clusters and the heterogeneity across different clusters. In this paper, we address these problems by proposing a class of score tests for the null hypothesis that all components of within- and between-subject variance are zeros in a kind of nonlinear mixed-effects model, and the asymptotic properties of the proposed tests are studied. The finite sample performance of this test is examined through simulation studies, and an illustrative example is presented.

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.

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