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Cantoni and Ronchetti (2001) introduced the concept of robust quasi-deviance based on the notion of robust quasi-likelihood function to evaluate the adequacy of a model.
Additionally, the ability to determine the adequacy of a model and the validation supporting it should be considered within a risk informed framework.
The usual method for testing the adequacy of a model is carried out by computing the F-ratio of the lack of fit to the pure error and comparing it with the standard value.
Notably, testing the adequacy of a model is not always straightforward.
The log likelihood ratio log [max{L (m1 = 0 | X)}/max{L (m1 | X)}] was -3.12, suggesting the adequacy of a model that includes migration.
In general, R (multiple correlation coefficient), CV (coefficient of variation) and P values are often used to evaluate the adequacy of a model.
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Special papers in statistics have been published in the oncology literature providing general introductions to survival analysis [ 5- 8]; topics covered included summarizing survival data, testing for a difference between groups, presenting existing statistical models, or assessing the adequacy of a survival model.
Structural equation modeling was used to explore the adequacy of a theoretical model linking topophilia and QOL.
In addition to using the log-likelihood ratio test to examine model significance, we used the c-statistic to evaluate the overall adequacy of a prediction model.
The adequacy of a mathematical model in this sense, however, is a matter of physical or engineering rather than mathematical judgment.
The Cumulative Residuals Method was also used to test the adequacy of a regression model throughout the range of each variable.
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