Bias Control for M-Quantile-Based Small Area Estimators

• 成果类型: Article
• 署名作者: Spagnolo, Francesco Schirripa; Salvati, Nicola; Bertarelli, Gaia; Haziza, David; Chambers, Ray
• 署名单位: University of Pisa; Universita Ca Foscari Venezia; University of Ottawa; Australian National University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2587316
• 发表日期: 2026-04-03
• 页码: 1671-1682
• 关键词: Continuous and discrete response variables; Mean squared prediction error; Predictive estimation; Projective estimation; Second-order unbiasedness property; mean squared error; Robust Estimation; prediction; regression; inference; models

摘要

Projective outlier-robust M-quantile-based small area estimators can be substantially biased when the sample data contain representative outliers. In this article we propose two new predictive type bias corrected versions of these estimators for continuous and discrete outcomes. Given both area level and individual level outliers in the population, these new estimators are more efficient than the robust-predictive and robust-projective estimators that have been proposed in the small area estimation literature. We also propose two estimators of the prediction mean-squared error of these estimators: one based on Taylor linearization and the other based on a new semi-parametric bootstrap method. We summarize the empirical evidence for these theoretical results in this article, while in the supplementary material we describe in more detail how the properties of these M-quantile-based small area estimators have been assessed in model-based and design-based simulations, as well as in a realistic application focusing on estimation of average income and unemployment rates for local labor market areas in Italy. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.