A1622
Title: Forward stability and model path selection
Authors: Lucas Mentch - University of Pittsburgh (United States) [presenting]
Abstract: Most scientific publications follow the familiar recipe of (i) obtaining data, (ii) fitting a model, and (iii) commenting on the scientific relevance of the effects of particular covariates in that model. This approach, however, ignores the fact that there may exist a multitude of similarly-accurate models in which the implied effects of individual covariates may be vastly different. This problem of finding an entire collection of plausible models has also received relatively little attention in the statistics community, with nearly all of the proposed methodologies being narrowly tailored to a particular model class and/or requiring an exhaustive search over all possible models, making them largely infeasible in the current big data era. The idea of forward stability is developed, and a novel, computationally-efficient approach is proposed to finding collections of accurate models referred to as model path selection (MPS). MPS builds up a plausible model collection via a forward selection approach and is entirely agnostic to the model class and loss function employed. The resulting model collection can be displayed in a simple and intuitive graphical fashion, easily allowing practitioners to visualize whether some covariates can be swapped for others with minimal loss.
