On the Use of Incomplete Prior Information in Regression Analysis

This article deals with the use of prior beliefs in the estimation of regression coefficients; in particular, it considers the problems that arise when the residual variance of the regression equation is unknown and it offers a large-sample solution. Additional contributions deal with testing the hy...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the American Statistical Association 1963-06, Vol.58 (302), p.401-414
Main Author: Theil, H.
Format: Article
Language:English
Subjects:
Covariance matrices
Eigenvalues
Estimators
Income estimates
Mathematical moments
Matrices
Price elasticity
Scalars
Standard error
Statistical variance
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article deals with the use of prior beliefs in the estimation of regression coefficients; in particular, it considers the problems that arise when the residual variance of the regression equation is unknown and it offers a large-sample solution. Additional contributions deal with testing the hypothesis that prior and sample information are compatible with each other; and with a scalar measure for the shares of these two kinds of information in the posterior precision.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.1963.10500854