Efficient Estimation of Nonlinear Finite Population Parameters Using Nonparametrics

TL;DR

This paper introduces a nonparametric, model-assisted method for accurately estimating nonlinear population parameters like Gini indices and low-income proportions, utilizing auxiliary data and providing consistent variance estimation.

Contribution

It develops a general nonparametric framework for estimating nonlinear parameters with auxiliary information, including practical implementation and variance estimation techniques.

Findings

Method performs well on French Labor Force Survey data.

Provides consistent variance estimators under mild assumptions.

Demonstrates advantages over parametric approaches for nonlinear parameters.

Abstract

Currently, the high-precision estimation of nonlinear parameters such as Gini indices, low-income proportions or other measures of inequality is particularly crucial. In the present paper, we propose a general class of estimators for such parameters that take into account univariate auxiliary information assumed to be known for every unit in the population. Through a nonparametric model-assisted approach, we construct a unique system of survey weights that can be used to estimate any nonlinear parameter associated with any study variable of the survey, using a plug-in principle. Based on a rigorous functional approach and a linearization principle, the asymptotic variance of the proposed estimators is derived, and variance estimators are shown to be consistent under mild assumptions. The theory is fully detailed for penalized B-spline estimators together with suggestions for practical implementation and guidelines for choosing the smoothing parameters. The validity of the method is demonstrated on data extracted from the French Labor Force Survey. Point and confidence intervals estimation for the Gini index and the low-income proportion are derived. Theoretical and empirical results highlight our interest in using a nonparametric approach versus a parametric one when estimating nonlinear parameters in the presence of auxiliary information.