Longitudinal data with nonstationary errors: a nonparametric three-stage approach

  1. Vicente Núñez-Antón 1
  2. Juan M. Rodríguez-Póo 2
  3. Philippe Vieu 3
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

  2. 2 Universidad de Cantabria
    info

    Universidad de Cantabria

    Santander, España

    ROR https://ror.org/046ffzj20

  3. 3 Paul Sabatier University
    info

    Paul Sabatier University

    Tolosa, Francia

    ROR https://ror.org/02v6kpv12

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Revista:
Test

ISSN: 1133-0686,  1863-8260

Año de publicación: 1999

Volumen: 8

Número: 1

Páginas: 201-231

Tipo: Artículo

DOI: 10.1007/BF02595870 WoS: WOS:000081757900019 GOOGLE SCHOLAR

Otras publicaciones en: Test

Resumen

We develop here a three-stage nonparametric method to estimate the common, group and individual effects in a longitudinal data setting. Our three-stage additive model assumes that the dependence between performance in an audiologic test and time is a sum of three components. One of them is the same for all individuals, the second one corresponds to the group effect and the last one to the individual effects. We estimate these functional components by nonparametric kernel smoothing techniques. We give theoretical results concerning rates of convergence of our estimates. This method is then applied to the data set that motivated the methods proposed here, the speech recognition data from the Iowa Cochlear Implant Project.