The limiting distribution of the maximum rank correlation estimator
TL;DR: In this article, the maximum rank correlation (MRC) estimator of Han's estimator is shown to be 4/5 consistent and asymptotically normal, based on a simple U-statistic decomposition and a uniform bound for degenerate U-processes.
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Abstract: Han's maximum rank correlation (MRC) estimator is shown to be 4/_-consistent and asymptotically normal. The proof rests on a general method for determining the asymptotic distribution of a maximization estimator, a simple U-statistic decomposition, and a uniform bound for degenerate U-processes. A consistent estimator of the asymptotic covariance matrix is provided, along with a result giving the explicit form of this matrix for any model within the scope of the MRC estimator. The latter result is applied to the binary choice model, and it is found that the MRC estimator does not achieve the semiparametric efficiency bound.
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Citations
An efficient semiparametric estimator for binary response models
Roger Klein,Richard H. Spady +1 more
TL;DR: In this article, an estimator for discrete choice models that makes no assumption concerning the functional form of the choice probability function, where this function can be characterized by an index, is proposed.
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Misclassification of the dependent variable in a discrete-response setting
TL;DR: In this paper, a modified maximum likelihood estimator that corrects for misclassification is proposed, which combines the maximum rank correlation estimator of Han (1987) (Journal of Econometrics 35, 303-316) with isotonic regression.
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Chapter 41 Estimation of semiparametric models
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Combining Predictors for Classification Using the Area under the Receiver Operating Characteristic Curve
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