Efficient estimation for marginal generalized partially linear single-index models with longitudinal data

• Peirong Xu ^[1] ; Jun Zhang ^[2] ; Xingfang Huang ^[1] ; Tao Wang ^[3]
  1. [1] Southeast University

     Southeast University

     China

     
  2. [2] Shenzhen University

     Shenzhen University

     China

     
  3. [3] Yale University

     Yale University

     Town of New Haven, Estados Unidos

     

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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. 25, Nº. 3, 2016, págs. 413-431
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • We consider marginal generalized partially linear single-index models for longitudinal data. A profile generalized estimating equations (GEE)-based approach is proposed to estimate unknown regression parameters. Within a wide range of bandwidths for estimating the nonparametric function, our profile GEE estimator is consistent and asymptotically normal even if the covariance structure is misspecified. Moreover, if the covariance structure is correctly specified, the semiparametric efficiency can be achieved under heteroscedasticity and without distributional assumptions on the covariates. Simulation studies are conducted to evaluate the finite sample performance of the proposed procedure. The proposed methodology is further illustrated through a data analysis.