Papers by Keyword: Random Effects

Abstract: Detection and estimation of structural change at unknown dates in panel model was investigated. The structural change can be consistently estimated and asymptotic distribution was derived. Monte Carlo simulations were presented to access the theoretical result numerically.

Abstract: In this study, the sample data was based on 2190 branch length samples of 30 trees from dahurian larch (Larix gmelinii Rupr.) plantations located in Wuying forest bureau in Heilongjiang Province. A second order polynomial equation with linear mixed-effects was used for modeling branch length of larch tree. The LME procedure in S-Plus is used to fit the mixed-effects models for the branch length data. The results showed that the polynomial model with three random parameters could significantly improve the model performance. The fitted mixed effects model was also evaluated using mean error, mean absolute error, mean percent error, and mean absolute percent error. The mixed model was found to predict branch length better than the original model fitted using ordinary least squares based on all errors. The application of mixed branch length model not only showed the mean trends of branch length, but also showed the individual difference based on variance-covariance structure.

Abstract: 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.