Fast Bayesian Variable Selection in Binomial and Negative Binomial\n Regression Article Swipe
Bayesian variable selection is a powerful tool for data analysis, as it\noffers a principled method for variable selection that accounts for prior\ninformation and uncertainty. However, wider adoption of Bayesian variable\nselection has been hampered by computational challenges, especially in\ndifficult regimes with a large number of covariates or non-conjugate\nlikelihoods. Generalized linear models for count data, which are prevalent in\nbiology, ecology, economics, and beyond, represent an important special case.\nHere we introduce an efficient MCMC scheme for variable selection in binomial\nand negative binomial regression that exploits Tempered Gibbs Sampling (Zanella\nand Roberts, 2019) and that includes logistic regression as a special case. In\nexperiments we demonstrate the effectiveness of our approach, including on\ncancer data with seventeen thousand covariates.\n
Related Topics
Concepts
Covariate
Binomial regression
Feature selection
Count data
Bayesian probability
Negative binomial distribution
Gibbs sampling
Markov chain Monte Carlo
Statistics
Econometrics
Bayesian linear regression
Variable (mathematics)
Computer science
Mathematics
Selection (genetic algorithm)
Logistic regression
Bayesian inference
Machine learning
Poisson distribution
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2106.14981
- https://arxiv.org/pdf/2106.14981
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4295677874
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4295677874Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2106.14981Digital Object Identifier
- Title
-
Fast Bayesian Variable Selection in Binomial and Negative Binomial\n RegressionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-28Full publication date if available
- Authors
-
Martin JankowiakList of authors in order
- Landing page
-
https://arxiv.org/abs/2106.14981Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2106.14981Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2106.14981Direct OA link when available
- Concepts
-
Covariate, Binomial regression, Feature selection, Count data, Bayesian probability, Negative binomial distribution, Gibbs sampling, Markov chain Monte Carlo, Statistics, Econometrics, Bayesian linear regression, Variable (mathematics), Computer science, Mathematics, Selection (genetic algorithm), Logistic regression, Bayesian inference, Machine learning, Poisson distribution, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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