An AIC-type information criterion evaluating theory-based hypotheses for contingency tables.

ABSTRACT: Researchers face inevitable difficulties when evaluating theory-based hypotheses in the context of contingency tables. Log-linear models are often insufficient to evaluate such hypotheses, as they do not provide enough information on complex relationships between cell probabilities in many real-life applications. These models are usually used to evaluate the relationships between variables using only equality restrictions between model parameters, while specifying theory-based hypotheses often also requires inequality restrictions. Moreover, high-dimensional contingency tables generally contain low cell counts and/or empty cells, complicating parameter estimation in log-linear models. The presence of many parameters in these models also causes difficulties in interpretation when evaluating

SUBMITTER: Altinisik Y 

PROVIDER: S-EPMC11754365 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature