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Confidence Regions for Parameters of Linear Models



Andrew L. Rukhin


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.
Statistica Sinica


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


Rukhin, A. (2010), Confidence Regions for Parameters of Linear Models, Statistica Sinica, [online], (Accessed June 22, 2024)


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Created April 1, 2010, Updated February 19, 2017