A goodness-of-fit test for regression models with spatially correlated errors

• Andrea Meilán-Vila ^[1] ; Jean D. Opsomer ^[3] ; Mario Francisco-Fernández ^[1] ; Rosa M. Crujeiras ^[2]
  1. [1] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
  2. [2] Universidade de Santiago de Compostela

     Universidade de Santiago de Compostela

     Santiago de Compostela, España

     
  3. [3] Westat, 1600 Research Boulevard, Rockville, MD, 20850, USA

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• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 29, Nº. 3, 2020, págs. 728-749
• Idioma: inglés
• DOI: 10.1007/s11749-019-00678-y
• Texto completo no disponible (Saber más ...)
• Resumen

  • The problem of assessing a parametric regression model in the presence of spatial correlation is addressed in this work. For that purpose, a goodness-of-fit test based on a L2-distance comparing a parametric and nonparametric regression estimators is proposed. Asymptotic properties of the test statistic, both under the null hypothesis and under local alternatives, are derived. Additionally, a bootstrap procedure is designed to calibrate the test in practice. Finite sample performance of the test is analyzed through a simulation study, and its applicability is illustrated using a real data example.

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