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Impact of incorrect assumptions on the covariance structure of random effects and/or residuals in nonlinear mixed models for repeated measures data
El Halimi, Rachid; Ocaña i Rebull, Jordi
Universitat de Barcelona
In this paper we analyse, using Monte Carlo simulation, the possible consequences of incorrect assumptions on the true structure of the random effects covariance matrix and the true correlation pattern of residuals, over the performance of an estimation method for nonlinear mixed models. The procedure under study is the well known linearization method due to Lindstrom and Bates (1990), implemented in the nlme library of S-Plus and R. Its performance is studied in terms of bias, mean square error (MSE), and true coverage of the associated asymptotic confidence intervals. Ignoring other criteria like the convenience of avoiding over parameterised models, it seems worst to erroneously assume some structure than do not assume any structure when this would be adequate.
2010-05-04
Estadística
Mètodes de simulació
Mètode de Montecarlo
Statistics
Simulation methods
Monte Carlo method
cc-by-nc-nd, (c) El Halimi et al., 2004
http://creativecommons.org/licenses/by-nc-nd/2.5/es/
Working Paper
         

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