AVERAGE DENSITY ESTIMATORS: EFFICIENCY AND BOOTSTRAP CONSISTENCY
AVERAGE DENSITY ESTIMATORS: EFFICIENCY AND BOOTSTRAP CONSISTENCY
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DOI：
10.1017/s0266466621000530
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发表时间：
2019-04
影响因子：
0.8
通讯作者：
M. D. Cattaneo;Michael Jansson
中科院分区：
文献类型：
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作者：
M. D. Cattaneo;Michael Jansson
本文强调了半参数效率和自举一致性之间的紧张局势的背景下，一个典型的半参数估计问题，即估计的平均密度的问题。结果表明，虽然简单的插件估计遭受偏见的问题，防止他们在最小的平滑条件下实现半参数效率，非参数引导自动纠正这种偏见，因此，这些看似低劣的估计实现引导一致性最小的平滑条件下。相比之下，几个“去偏”估计，实现半参数的最小光滑条件下的效率不实现自举一致性在这些相同的条件下。
This paper highlights a tension between semiparametric efficiency and bootstrap consistency in the context of a canonical semiparametric estimation problem, namely the problem of estimating the average density. It is shown that although simple plug-in estimators suffer from bias problems preventing them from achieving semiparametric efficiency under minimal smoothness conditions, the nonparametric bootstrap automatically corrects for this bias and that, as a result, these seemingly inferior estimators achieve bootstrap consistency under minimal smoothness conditions. In contrast, several “debiased” estimators that achieve semiparametric efficiency under minimal smoothness conditions do not achieve bootstrap consistency under those same conditions.