Bayes Estimates for the Linear Model
Abstract
Summary The usual linear statistical model is reanalyzed using Bayesian methods and the concept of exchangeability. The general method is illustrated by applications to two-factor experimental designs and multiple regression.
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Lindley and Smith (1972) showed that allowing the data to estimate the prior variance of parameters is essentially what ridge regression does for linear regression.
“Lindley and Smith (1972) showed that this is essentially what ridge regression does for linear regression”
From Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors (1999) · cites this paper as Lindley and Smith (1972)
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- Authors
- Lindley, D. V., & Smith, A. F. M.
- Affiliations
- University College London (GB)
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1972-09-01
- Volume
- 34
- Issue
- 1
- Pages
- 1-18
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W128740895
- Cited by
- 2,025
- References
- 0
- Citations by year
- 2026: 23 · 2025: 36 · 2024: 23 · 2023: 37 · 2022: 35 · 2021: 49 · 2020: 34 · 2019: 41 · 2018: 38 · 2017: 41 · 2016: 55 · 2015: 75 · 2014: 71 · 2013: 45 · 2012: 89
- FWCI
- 27.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- Bayesian estimation, linear statistical models, exchangeability, multiple linear regression, prior distributions
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