Automatic smoothing parameter selection in non-parametric models for longitudinal data

Kiros Berhane, J. Sunil Rao

Research output: Contribution to journalArticle


The selection of smoothing parameters by generalized cross-validation (GCV) becomes complicated when dealing with correlated data. In this paper, we develop an automatic algorithm for selection of smoothing parameters in non-parametric longitudinal models by combining the BRUTO algorithm of Hastie (1989) and the modifications to GCV due to Altman (1990) to handle the correlation. The algorithm is detailed and illustrated via analysis of a panic-attack data set.

Original languageEnglish (US)
Pages (from-to)289-296
Number of pages8
JournalApplied Stochastic Models and Data Analysis
Issue number3-4
StatePublished - Sep 1 1997



  • Correlated data
  • Cross validation
  • Generalized estimating equations
  • Local-scoring
  • Quasi-likelihood
  • Smoothing

ASJC Scopus subject areas

  • Management of Technology and Innovation
  • Modeling and Simulation

Cite this