{"ID":2834205,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.03266","arxiv_id":"2512.03266","title":"Invited Discussion of \"Model Uncertainty and Missing Data: An Objective Bayesian Perspective\" by Gonzalo García-Donato , María Eugenia Castellanos , Stefano Cabras Alicia Quirós , and Anabel Forte","abstract":"The article by Garc{í}a-Donato and co-authors addresses the dual challenges of accounting for model uncertainty and missing data within the Gaussian regression frameworks from an objective Bayesian perspective. Thru the use of an imputation $g$-prior that replaces $X_γ^TX_γ$ for model $γ$ in the covariance of $β_γ$ with $Σ_{X_γ}$, the authors develop a coherent approach to addressing the missing data problem and model uncertainty simultaneously with random $X_γ$ in the missing at random (MAR) or missing completely at random (MCAR) settings, while still being computationally tractable. I discuss the connection of the imputation $g$-prior to the $g$-prior with imputed $X$, and to model selection for graphical models that provide an alternative justification for the $g$-prior for random $X$s.","short_abstract":"The article by Garc{í}a-Donato and co-authors addresses the dual challenges of accounting for model uncertainty and missing data within the Gaussian regression frameworks from an objective Bayesian perspective. Thru the use of an imputation $g$-prior that replaces $X_γ^TX_γ$ for model $γ$ in the covariance of $β_γ$ wit...","url_abs":"https://arxiv.org/abs/2512.03266","url_pdf":"https://arxiv.org/pdf/2512.03266v1","authors":"[\"Merlise A Clyde\"]","published":"2025-12-02T22:15:13Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"math.ST\",\"stat.OT\"]","methods":"[]","has_code":false}