2015/11/30 by Ryan Martin · 31 citations
Computer Science · Mathematics · #Advanced Database Systems and Queries #Algebra over a field #Algorithm #Applied mathematics #Artificial intelligence #Bayesian Modeling and Causal Inference #Calculus (dental) #Computation #Computer science #Data Management and Algorithms #Generalized linear model #Inference #Mathematics #Process (computing) #Programming language #Pure mathematics #Representation (politics) #Theoretical computer science #stat.ME
paper · pdf · doi:10.1016/j.jspi.2016.11.006
published in Journal of Statistical Planning and Inference 195, 105-115 (Elsevier BV) · 18 pages, 4 figures
arxiv created 2016/06/16 · openalex publication_date 2016/12/08 · arxiv updated 2019/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The inferential model (IM) approach, like fiducial and its generalizations, depends on a representation of the data-generating process. Here, a particular variation on the IM construction is considered, one based on generalized associations. The resulting generalized IM is more flexible than the basic IM in that it does not require a complete specification of the data-generating process and is provably valid under mild conditions. Computation and marginalization strategies are discussed, and two applications of this generalized IM approach are presented.