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

Kiros Berhane, J. Sunil Rao

Research output: Contribution to journalArticlepeer-review

Abstract

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
Volume13
Issue number3-4
DOIs
StatePublished - 1997
Externally publishedYes

Keywords

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

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