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December 2018 Extremal quantile treatment effects
Yichong Zhang
Ann. Statist. 46(6B): 3707-3740 (December 2018). DOI: 10.1214/17-AOS1673

Abstract

This paper establishes an asymptotic theory and inference method for quantile treatment effect estimators when the quantile index is close to or equal to zero. Such quantile treatment effects are of interest in many applications, such as the effect of maternal smoking on an infant’s adverse birth outcomes. When the quantile index is close to zero, the sparsity of data jeopardizes conventional asymptotic theory and bootstrap inference. When the quantile index is zero, there are no existing inference methods directly applicable in the treatment effect context. This paper addresses both of these issues by proposing new inference methods that are shown to be asymptotically valid as well as having adequate finite sample properties.

Citation

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Yichong Zhang. "Extremal quantile treatment effects." Ann. Statist. 46 (6B) 3707 - 3740, December 2018. https://doi.org/10.1214/17-AOS1673

Information

Received: 1 February 2017; Revised: 1 November 2017; Published: December 2018
First available in Project Euclid: 11 September 2018

zbMATH: 06965702
MathSciNet: MR3852666
Digital Object Identifier: 10.1214/17-AOS1673

Subjects:
Primary: 62E20 , 62G32
Secondary: 62P20

Keywords: Extreme quantile , intermediate quantile

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.46 • No. 6B • December 2018
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