Concentration inequalities for Gauss-Markov estimators
Let M be the regression subspace and [gamma] the set of possible covariances for a random vector Y. The linear model determined by M and [gamma] is regular if the identity is in [gamma] and if [Sigma](M)[subset, double equals]M for all [Sigma][set membership, variant][gamma]. For such models, concentration inequalities are given for the Gauss-Markov estimator of the mean vector under various distributional and invariance assumptions on the error vector. Also, invariance is used to establish monotonicity results relative to a natural group induced partial ordering.
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Volume (Year): 25 (1988)
Issue (Month): 1 (April)
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