Fundamentals of non-parametric statistical inference for integrated quantiles

TL;DR

This paper develops a comprehensive non-parametric inference framework for integrated quantiles, applicable to various sampling schemes and dependence structures, and demonstrates its utility through classical estimators and tail risk measures.

Contribution

It introduces a general theory for non-parametric inference of integrated quantiles, including tail-value-at-risk, Lorenz, and Gini curves, with minimal assumptions and broad applicability.

Findings

Proves consistency and asymptotic normality of estimators under simple random sampling.

Provides new proofs for classical estimators like trimmed means.

Extends results to dependent data such as time series.

Abstract

We present a general non-parametric statistical inference theory for integrals of quantiles without assuming any specific sampling design or dependence structure. Technical considerations are accompanied by examples and discussions, including those pertaining to the bias of empirical estimators. To illustrate how the general results can be adapted to specific situations, we derive - at a stroke and under minimal conditions - consistency and asymptotic normality of the empirical tail-value-at-risk, Lorenz and Gini curves at any probability level in the case of the simple random sampling, thus facilitating a comparison of our results with what is already known in the literature. Results, notes and references concerning dependent (i.e., time series) data are also offered. As a by-product, our general results provide new and unified proofs of large-sample properties of a number of classical statistical estimators, such as trimmed means, and give additional insights into the origins of, and the reasons for, various necessary and sufficient conditions.