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Confidence Regions for Parameters of Linear Models

Published

Author(s)

Andrew L. Rukhin

Abstract

A method is suggested for constructing a conservative confidence region for the parameters of a general linear model. In meta-analytical applications, when the results of independent but heterogeneous studies are to be combined, this region can be employed with little to no knowledge of error variances. The required optimization problem is formulated and some properties of its solution are found.
Citation
Statistica Sinica

Keywords

Cauchy-Binet formula, Dirichlet averages, General linear model, meta-analysis, weighted bootstrap and jackknife variance estimators, zonal polynomials, Schur-convexity

Citation

Rukhin, A. (2010), Confidence Regions for Parameters of Linear Models, Statistica Sinica, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=152120 (Accessed June 22, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created April 1, 2010, Updated February 19, 2017