A one-bring-one route for assessing the uncertainty of small area estimation in nested-error regression models

• Yuzi Liu ^[1] ; Haiqiang Ma ^[1] ; Xiaohui Liu ^[1] ; Jiming Jiang ^[2]
  1. [1] Jiangxi University of Finance and Economics

     Jiangxi University of Finance and Economics

     China

     
  2. [2] University of California System

     University of California System

     Estados Unidos

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 34, Nº. 2, 2025, págs. 383-430
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • The nested-error regression (NER) models are widely used to analyze unit-level data in small area estimation. Concerned about possible model misspecification, Jiang et al. (Surv Methodol 41:37–55, 2015) suggested a new prediction procedure, entitled observed best prediction (OBP), for the NER models and showed its desirable properties under such a setting. However, how to assess the uncertainty of OBP in such a case remains poorly addressed. This paper investigates this issue by developing a new estimator relying on the so-called one-bring-one route. It is shown that the new estimator is second-order unbiased under some mild conditions. Some simulations are conducted to confirm its finite sample performance. Finally, we applied the proposed estimator to a real-data example.