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Unlike Poisson models, NBREG models correct for this over-dispersion by including a parameter that accounts for unobserved heterogeneity (Long and Freese 2001).
Even though NBR and ZIP models correct for equality of the conditional mean and variance of the distribution, they also induce an over-dispersion problem (Mullahy 1986; Greene 2002).
Such models correct for the correlation between repeated observations with subjects.
GEE models correct for non-independence of observations among the same group (health center), assuming a priori a certain structure of correlation for the dependent variable measured in each group.
These fusion models correct for the biases inherent in atmospheric dispersion models, and also provide associated measures of uncertainty for the predictions, which are typically not available from dispersion models.
First, as seen in the case of Northern Rock, there is frequently an economic boom prior to a bank system crisis, which may lead to artificially higher or lower mortality rates [ 10]; hence, models correct for the previous year's change in real gross domestic product per capita.
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They estimate both cross-sectional and fixed effects models correcting for unobserved ability effects, but they do not perform separate analyses for natives and immigrants.
Both the logistic regression and linear regression models corrected for age, and Acute Physiology and Chronic Health Evaluation (APACHE) II score.
All models corrected for clustering by clinic.
The second and third models corrected for both kinship and population structure.
Table 2 presents Yule-Walker estimates of two different autoregressive error models corrected for third-order autocorrelation.
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