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On the construction of efficient estimators in semiparametric models

机译:关于半参数模型中有效估计的构造

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摘要

This paper deals with the construction of efficient estimators in semiparametric models without the sample splitting technique. Schick (1987) gave sufficient conditions using the leave-one-out technique for a construction without sample splitting. His conditions are stronger and more cumbersome to verify than the necessary and sufficient conditions for the existence of efficient estimators which suffice for the construction based on sample splitting. In this paper we use a conditioning argument to weaken Schick's conditions. We shall then show that in a large class of semiparametric models and for properly chosen estimators of the score function the resulting weaker conditions reduce to the minimal conditions for the construction with sample splitting. In other words, in these models efficient estimators can be constructed without sample splitting under the same conditions as those used for the construction with sample splitting. We demonstrate our results by constructing an efficient estimator using these ideas in a semiparametric additive regression model.
机译:本文研究了不带样本拆分技术的半参数模型中有效估计量的构造。 Schick(1987)使用留一法技术为没有样品分裂的构造提供了足够的条件。他的条件比存在有效估计量的必要和充分条件更强大,更麻烦,因为有效估计量的存在足以满足基于样本拆分的构造。在本文中,我们使用条件论证来削弱Schick的条件。然后,我们将证明,在一大类半参数模型中,并且对于得分函数的正确选择的估计器,所得到的较弱条件会减少到用于进行样本拆分的构造的最小条件。换句话说,在这些模型中,可以在与用于样本拆分构造的条件相同的条件下构造有效估计量,而无需样本拆分。我们通过在半参数加性回归模型中使用这些思想构造一个有效的估计量来证明我们的结果。

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