The purpose of statistical model selection is to identify a parsimonious model, which is a model that is as simple as possible while maintaining good predictive ability over the outcome of interest.

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

This issue of The Journal of Risk Model Validation features two papers that directly address validation using machine learning. Whether their findings imply we will all (including the editor) become ...

Take advantage of automatic model state validation in ASP.Net Core to avoid tedious manual coding for HTML form validation When we create applications that accept data from users, we must validate ...

When working with applications in ASP.NET Core 6 you will often want to validate your models to ensure that the data they contain conform to the pre-defined validation rules. Enter model validation.