Regularized regression models are well studied and, under appropriate conditions, offer fast and statistically interpretable results. However, large data in many applications are heterogeneous in the sense of harboring distributional differences between latent groups. Then, the assumption that the conditional distribution of response Y given features X is the same for all samples may not hold....
The cause of failure in cohort studies that involve competing risks is frequently incompletely observed. Failure to deal with this issue can lead to substantially biased estimates. To the best of our knowledge, all the methods that have addressed the issue in the context of semiparametric competing risks models rely on a missing at random (MAR) assumption. Nevertheless, the MAR assumption is...
We consider a Bayesian approach for the analysis of rating data when a scaling component is taken into account, thus incorporating a specific form of heteroskedasticity. Our approach includes model-based probability effect measures that enable comparisons of distributions among multiple groups. These effect measures are adjusted for explanatory variables that have an impact on both the...
The contribution aims at discussing some preliminary results on the evaluation of prediction performance for the class of mixture models with uncertainty (Piccolo and Simone, 2019). The ultimate goal of the analysis is the evaluation of the extent by which the uncertainty specification constitutes an added value for prediction of ordinal scores. A small simulation study is presented to assess...
