Abstract
This study identifies an efficient method for the main interaction between categorical variables in multi-dimensional contingency tables. LASSO and Stepwise Algorithms are cross-validation methods for finding the penalty coefficient and model selection method. The methods used the Akaike Information Criterion (AIC) and p-value as indicators for selecting parameters to be included in the model. The aims of the study are to review the literature related to multi-dimensional contingency tables with log-linear models and high dimensional tables; to analyse the obesity dataset from Locksmith (GSK) GP Research Database from around the year 2000, where the dataset is composed of p = 10 binary comorbidities in n = 5000 patients using the models; and lastly, to compare the results obtained from the models. Stepwise Algorithms is an appropriate method for finding the parsimonious interaction structure between the categorical variables. The method defines a continuous shrinking operation that can produce coefficients which are exactly zero.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
