SIMEX parameter estimates

Figure 5.3 Scatter plot of SIMEX parameter estimates when the coefficient of variation in the measurement error was varied from 0 to 20% based on the data in Table 5.3.

The same caveats that apply to linear models when the predictor variables are measured with error apply to nonlinear models. When the predictor variables are measured with error, the parameter estimates may become biased, depending on the nonlinear model. Simulation may be used as a quick test to examine the dependency of parameter estimates within a particular model on measurement error (Fig. 3.14). The SIMEX algorithm, as introduced in the chapter on Linear Models and Regression, can easily be extended to nonlinear models, although the computation time will increase by orders of magnitude. 

With this as an estimate of the assay measurement variance, the SIMEX algorithm was applied. Figure 2.10 plots the mean regression parameter against varying values of X using 1000 iterations for each value of X. Extrapolation of X to — 1 for both the slope and intercept leads to a SIMEX equation of... 

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