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On the Asymptotics of Constrained M-Estimation
Charles J. Geyer
The Annals of Statistics
Vol. 22, No. 4 (Dec., 1994), pp. 1993-2010
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/2242495
Page Count: 18
You can always find the topics here!Topics: Tangents, Maximum likelihood estimation, Perceptron convergence procedure, Central limit theorem, Topological theorems, Mathematical functions, Asymptotic value, Quadrants, Statistics
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Limit theorems for an M-estimate constrained to lie in a closed subset of Rd are given under two different sets of regularity conditions. A consistent sequence of global optimizers converges under Chernoff regularity of the parameter set. A $\sqrt n$-consistent sequence of local optimizers converges under Clarke regularity of the parameter set. In either case the asymptotic distribution is a projection of a normal random vector on the tangent cone of the parameter set at the true parameter value. Limit theorems for the optimal value are also obtained, agreeing with Chernoff's result in the case of maximum likelihood with global optimizers.
The Annals of Statistics © 1994 Institute of Mathematical Statistics