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A Semi-Parametric Basis for Combining Estimation Problems Under Quadratic Loss AgEcon
Judge, George G.; Mittelhammer, Ronald C..
When there is uncertainty concerning the appropriate statistical model to use in representing the data sampling process and corresponding estimators, we consider a basis for optimally combining estimation problems. In the context of the multivariate linear statistical model, we consider a semi-parametric Stein-like (SPSL) estimator, ...that shrinks to a random data-dependent vector and, under quadratic loss, has superior performance relative to the conventional least squares estimator. The relationship of the SPSL estimator to the family of Stein estimators is noted and risk dominance extensions between correlated estimators are demonstrated. As an application we consider the problem of a possibly ill-conditioned design matrix and devise...
Tipo: Working or Discussion Paper Palavras-chave: Stein-like shrinkage; Quadratic loss; Ill-conditioned design; Semiparametric estimation and inference; Data dependent shrinkage vector; Asymptotic and finite sample risk.; Research Methods/ Statistical Methods; Cl0; C24.
Ano: 2003 URL: http://purl.umn.edu/25103
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