Penalized spline estimation in varying coefficient models with censored data

• K. Hendrickx ^[1] ; P. Janssen ^[1] ; A. Verhasselt ^[1]
  1. [1] University of Hasselt

     University of Hasselt

     Arrondissement Hasselt, Bélgica

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 27, Nº. 4, 2018, págs. 871-895
• Idioma: inglés
• DOI: 10.1007/s11749-017-0574-y
• Texto completo no disponible (Saber más ...)
• Resumen

  • We consider P-spline smoothing in a varying coefficient regression model when the response is subject to random right censoring. We introduce two data transformation approaches to construct a synthetic response vector that is used in a penalized least squares optimization problem. We prove the consistency and asymptotic normality of the P-spline estimators for a diverging number of knots and show by simulation studies and real data examples that the combination of a data transformation for censored observations with P-spline smoothing leads to good estimators of the varying coefficient functions