Likelihood ratio tests are statistical tests used to compare the fit of two models, typically a null hypothesis model against an alternative model, by assessing the ratio of their likelihoods. This method provides a powerful way to evaluate whether the additional parameters in the alternative model significantly improve the fit to the data, making it particularly useful in nonlinear regression models. By examining how well each model explains the observed data, these tests can help determine which model is more appropriate for a given dataset.
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