Empirical likelihood inference for generalized additive partially linear models

• Rong Liu ^[1] ; Yichuan Zhao ^[2]
  1. [1] University of Toledo

     University of Toledo

     City of Toledo, Estados Unidos

     
  2. [2] Georgia State University

     Georgia State University

     Estados Unidos

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 30, Nº. 3, 2021, págs. 569-585
• Idioma: inglés
• DOI: 10.1007/s11749-020-00731-1
• Texto completo no disponible (Saber más ...)
• Resumen

  • Generalized additive partially linear models enjoy the simplicity of GLMs and the flexibility of GAMs because they combine both parametric and nonparametric components. Based on spline-backfitted kernel estimator, we propose empirical likelihood (EL)-based pointwise confidence intervals and simultaneous confidence bands (SCBs) for the nonparametric component functions to make statistical inference. Simulation study strongly supports the asymptotic theory and shows that EL-based SCBs are much easier for implementation and have better performance than Wald-type SCBs. We apply the proposed method to a university retention study and provide SCBs for the effect of the students information.