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- W2116769867 abstract "The smoothed maximum score estimator of the coefficient vector of a binary response model is consistent and, after centering and suitable normalization, asymptotically normally distributed under weak assumptions [5]. Its rate of convergence in probability is N − h /(2 h +1) , where h ≥ 2 is an integer whose value depends on the strength of certain smoothness assumptions. This rate of convergence is faster than that of the maximum score estimator of Manski [11,12], which converges at the rate N −1/3 under assumptions that are somewhat weaker than those of the smoothed estimator. In this paper I prove that under the assumptions of smoothed maximum score estimation, N − h /(2 h +1) is the fastest achievable rate of convergence of an estimator of the coefficient vector of a binary response model. Thus, the smoothed maximum score estimator has the fastest possible rate of convergence. The rate of convergence is defined in a minimax sense so as to exclude superefficient estimators." @default.
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- W2116769867 date "1993-01-01" @default.
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- W2116769867 title "Optimal Rates of Convergence of Parameter Estimators in the Binary Response Model with Weak Distributional Assumptions" @default.
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- W2116769867 doi "https://doi.org/10.1017/s0266466600007301" @default.
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