ArticleStatistica Sinica2009
An Information Matrix Prior for Bayesian Analysis in Generalized Linear Models with High Dimensional Data.
Article in Statistica Sinica, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Bayesian bi-level variable selection for genome-wide survival study.Genomics & informatics · 2023Article
- A New Regression Model for the Analysis of Overdispersed and Zero-Modified Count Data.Entropy (Basel, Switzerland) · 2021Article
- A bayesian framework that integrates heterogeneous data for inferring gene regulatory networks.Frontiers in bioengineering and biotechnology · 2014Review
- Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology.BMC systems biology · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
An important challenge in analyzing high dimensional data in regression settings is that of facing a situation in which the number of covariates p in the model greatly exceeds the sample size n (sometimes termed the "p > n" problem). In this article, we develop a novel specification for a general class of prior distributions, called Information Matrix (IM) priors, for high-dimensional generalized linear models. The priors are first developed for settings in which p < n, and then extended to the p > n case by defining a ridge parameter in the prior construction, leading to the Information Matrix Ridge (IMR) prior. The IM and IMR priors are based on a broad generalization of Zellner's g-prior for Gaussian linear models. Various theoretical properties of the prior and implied posterior are derived including existence of the prior and posterior moment generating functions, tail behavior, as well as connections to Gaussian priors and Jeffreys' prior. Several simulation studies and an application to a nucleosomal positioning data set demonstrate its advantages over Gaussian, as well as g-priors, in high dimensional settings.
Identifiers
20664718PMC2909687What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.