Methodological Advances in Robust Small Area Estimation

• María Bugallo Porto ^[1] ; Domingo Morales González ^[1]
  1. [1] Universidad Miguel Hernández de Elche

     Universidad Miguel Hernández de Elche

     Elche, España

     
• Localización: Proceedings XoveTIC 2025: Impulsando el talento científico / coord. por Manuel Lagos Rodríguez, Hilda Romero-Velo, Álvaro Leitao , Javier Pereira-Loureiro , Manuel Penedo , 2026, ISBN 978-84-9749-925-5, págs. 3-10
• Idioma: inglés
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• Resumen

  • Small Area Estimation techniques address the growing demand for reliable disag-gregated statistics by fitting models to unit-level or area-level data. While recent advances inunit-level mixed models are significant, the presence of outliers has driven the development ofrobust methods. M-quantile regression offers a promising alternative to mixed models for SmallArea Estimation, though some theoretical aspects remain underexplored. We cover the optimalselection of the robustness parameters for bias correction, improving outlier detection. In addi-tion, we propose bootstrap methods to approximate the distribution of area-specific M-quantilecoefficients and optimal robustness parameters, enhancing inference and diagnostics.