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The model comparison statistic has a Chi-squared distribution with the degree of freedom (df) equal to the difference in number of parameters [ 27].
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A model comparison test statistic, T, is created from the best-fit statistics of each fit; as with all statistics, it is sampled from a probability distribution p(T).
ROC curves plot the true positive rate and false positive rate of a classifier over a range of threshold values, and the area under the curve (AUC) is a traditionally used statistic for model comparison.
For model comparison, we used the BF 'test statistic' of 2 log(marginal likelihood [unconstrained model]) – log(marginal likelihood [constrained model]).
To identify best-fitting models, terms were sequentially reduced from the full model and model comparison was achieved with the F statistic.
Metastatic data were analysed using the Kaplan Meier product limit estimator (Unistat), with the log-rank comparison statistic, and by the Cox's proportional-hazard model (Stata).
Then, as an illustration, by considering the model comparison hypothesis defined by the weighted Euclidean norm of moment restrictions, we propose a feasible approximate test statistic to the optimal one and study its asymptotic properties.
The area-under-curve (AUC) statistic of ROC is commonly used in machine learning and data mining community for model comparison.
That's model comparison.
Model comparison.
The -2 Log-Likelihood statistic and two commonly used penalized model selection criteria, the Bayesian information criterion (BIC) and Akaike's information criterion (AIC), were used for model comparison.
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