Abstract
Before making inferences about a population using a fitted model, it is necessary to determine whether the fitted model describes the data well. A poorly fitted model may lead to biased and invalid conclusions, resulting in incorrect inferences. Recent studies show the necessity of goodness-of-fit tests for high level binary multilevel models. The focus here was to develop a goodness-of-fit test to use in the model adequacy testing of high level binary multilevel models and to examine, whether the type I error and power hold for the newly developed goodness-of-fit test considering a three-level random intercept model.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Communications in Statistics - Simulation and Computation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
