In the intricate dance of balancing efficiency and performance within AI projects, the selection among sparse, small and large models isn't just a technical decision—it's a strategic imperative that ...

In this paper, we introduce a novel model selection approach to time series forecasting. For linear stationary processes, such as AR processes, the direction of time is independent of the model ...

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.

Bayesian model selection offers a coherent framework for identifying the most plausible models when the number of candidate predictors greatly exceeds the number of observations. Central to this ...