A Nested Error Regression Model with High Dimensional Parameter for Small Area Estimation

TL;DR

This paper introduces a flexible nested error regression model with high-dimensional parameters for small area estimation, incorporating heterogeneity and providing robust estimation methods for uncertainty measures.

Contribution

It develops a novel small area model with high-dimensional parameters and a robust estimation approach for uncertainty quantification.

Findings

Effective pooling of large area data achieved

Model performs well in simulations and real data

Provides accurate MSE and CV estimates

Abstract

In this paper we propose a flexible nested error regression small area model with high dimensional parameter that incorporates heterogeneity in regression coefficients and variance components. We develop a new robust small area specific estimating equations method that allows appropriate pooling of a large number of areas in estimating small area specific model parameters. We propose a parametric bootstrap and jackknife method to estimate not only the mean squared errors but also other commonly used uncertainty measures such as standard errors and coefficients of variation. We conduct both modelbased and design-based simulation experiments and real-life data analysis to evaluate the proposed methodology