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Bootstrap model building from

Convergence was successful for 502 of the 505 bootstrap data sets. Three data sets persisted in terminating with rounding errors despite the application of several sets of starting parameters. The results of the bootstrap parameter estimates are presented in Table 15.2 and compared to the results from the PPK model building process. There is strong evidence that the model is without substantive deficiencies and should be accepted as the final irreducible model. [Pg.416]

For example, we can build B = 400 parametric bootstrap samples, each by adding noise to the model for predicting y from the last example. We assume the noise, denoted s has a Gaussian distribution with mean zero and variance = YH=i( yi y<9 In, written as a,- A1(0, ri). Then we can write y = a -1-/3/fc > 0. 5) -b Si, where a and /3 are the maximum-UkeUhood estimates of the intercept and slope, respectively. As B increases, the parametric bootstrap samples capture the variability of the estimates for a and a -b /3. For example, as with the nonparametric bootstrap, with B = 400, we can extract a 95% confidence interval for the estimates by selecting the 0.025 and 0.975 quantiles of the bootstrap estimates for each z-value. [Pg.238]


See other pages where Bootstrap model building from is mentioned: [Pg.126]    [Pg.295]    [Pg.396]    [Pg.115]    [Pg.371]    [Pg.266]    [Pg.452]    [Pg.46]   
See also in sourсe #XX -- [ Pg.411 ]




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