A Note on Regression When There is Extraneous Information About One of the Coefficients

Durbin, J.

doi:10.1080/01621459.1953.10501201

Cited by 144Advanced Statistical Methods and ModelsStatistical and numerical algorithmsStatistical Methods and Applications

A Note on Regression When There is Extraneous Information About One of the Coefficients

Durbin, J.·1953·Journal of the American Statistical Association

DOI 10.1080/01621459.1953.10501201

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Abstract

(1953). A Note on Regression When There is Extraneous Information About One of the Coefficients. Journal of the American Statistical Association: Vol. 48, No. 264, pp. 799-808.

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Eq. (2) can be fitted subject to linear equality restrictions, which would reduce the number of parameters to be estimated.
“First, Eq. (2) can be fitted subject to linear equality restrictions, which would reduce the number of parameters to be estimated [56,57].”
From Syndemics and Health Disparities: A Methodological Note (2016) · cites this paper as [56]

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Authors: Durbin, J.
Affiliations: London School of Economics and Political Science (GB)
Corresponding author: Durbin, J.
Venue: Journal of the American Statistical Association
Publisher: Informa UK Limited
Published: 1953-12
Volume: 48
Issue: 264
Pages: 799-808
ISSN: 0162-1459 (ISSN-L)
1537-274X
DOI: 10.1080/01621459.1953.10501201
OpenAlex: W1983547829
Cited by: 143
References: 3
Citations by year: 2026: 1 · 2025: 1 · 2024: 5 · 2023: 3 · 2022: 6 · 2021: 1 · 2020: 4 · 2019: 1 · 2018: 3 · 2017: 2 · 2016: 3 · 2015: 5 · 2014: 7 · 2013: 5 · 2012: 2
FWCI: 1.56
Type: journal article
Language: en
Open access: closed
Locations: Journal of the American Statistical Association (publishedVersion)
UCL Discovery (University College London) (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: regression analysis, extraneous information, Bayesian regression, prior distributions, parameter estimation

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A Note on Regression When There is Extraneous Information About One of the Coefficients

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