Assessing Tuning Parameter Selection Variability in Penalized Regression.

TitleAssessing Tuning Parameter Selection Variability in Penalized Regression.
Publication TypeJournal Article
Year of Publication2019
AuthorsHu, Wenhao, Eric B. Laber, Clay Barker, and Leonard A. Stefanski
Date Published2019

Penalized regression methods that perform simultaneous model selection and estimation are ubiquitous in statistical modeling. The use of such methods is often unavoidable as manual inspection of all possible models quickly becomes intractable when there are more than a handful of predictors. However, automated methods usually fail to incorporate domain-knowledge, exploratory analyses, or other factors that might guide a more interactive model-building approach. A hybrid approach is to use penalized regression to identify a set of candidate models and then to use interactive model-building to examine this candidate set more closely. To identify a set of candidate models, we derive point and interval estimators of the probability that each model along a solution path will minimize a given model selection criterion, for example, Akaike information criterion, Bayesian information criterion (AIC, BIC), etc., conditional on the observed solution path. Then models with a high probability of selection are considered for further examination. Thus, the proposed methodology attempts to strike a balance between algorithmic modeling approaches that are computationally efficient but fail to incorporate expert knowledge, and interactive modeling approaches that are labor intensive but informed by experience, intuition, and domain knowledge. Supplementary materials for this article are available online.

Alternate JournalTechnometrics
Original PublicationAssessing tuning parameter selection variability in penalized regression.
PubMed ID31534281
PubMed Central IDPMC6750234
Grant ListK22 LM011430 / LM / NLM NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States