Ordinal Outcome Modeling: The Application of the Adaptive Moment Estimation Optimizer to the Elastic Net Penalized Stereotype Logit

Abstract

Penalized ordinal outcome models were developed to model high dimensional data with ordinal outcomes. One option is the penalized stereotype logit, which includes nonlinear combinations of parameter estimates. Optimization algorithms assuming linearity and function convexity were applied to fit this model. In this study the application of the adaptive moment estimation (Adam) optimizer, suited for nonlinear optimization, to the elastic net penalized stereotype logit model is proposed. The proposed model is compared to the L1 penalized ordinalgmifs stereotype model. Both methods were applied to simulated and real data, with non-Hodgkin lymphoma (NHL) cancer subtypes as the outcome, with results presented and discussed.