This article studies an asymptotic framework for conducting inference on parameters of the
form, where is a known directionally differentiable function and is estimated by. In these …

This paper provides a numerical derivative based Delta method that complements the recent
work by Fang and Santos (2014) and also generalizes a previous insight by Song (2014) …

This paper studies an asymptotic framework for conducting inference on parameters of the
form $\phi (\theta_0) $, where $\phi $ is a known directionally differentiable function and …

In this paper, I study the statistical inference problem for treatment assignment policies. In
typical applications, individuals with different characteristics are expected to differ in their …

This paper presents a unified framework for inference on parameters of the form ϕ (θ 0),
where θ 0 is unknown but can be estimated by θ ˆ n, and ϕ is known with null first order …

This paper studies optimal estimation of parameters taking the form φ (θ0), where θ0 is
unknown but can be regularly estimated and φ is a known directionally differentiable …

In a decision-theoretic framework, minimax lower bound provides the worst-case
performance of estimators relative to a given class of statistical models. For parametric and …

Policymakers often desire a statistical treatment rule (STR) that determines a treatment
assignment rule deployed in a future population from available data. With the true …

This paper studies the identification and estimation of weighted average derivatives of
conditional location functionals including conditional mean and conditional quantiles in …

We study inference for parameters defined by either classical extremum estimators or
Laplace-type estimators subject to general nonlinear constraints on the parameters. We …