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

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  • Kiros Berhane
  • J. Sunil Rao

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. © 1998 John Wiley & Sons, Ltd.

Suggested Citation

  • Kiros Berhane & J. Sunil Rao, 1997. "Automatic smoothing parameter selection in non‐parametric models for longitudinal data," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 13(3‐4), pages 289-296, September.
    • Handle: RePEc:wly:apsmda:v:13:y:1997:i:3-4:p:289-296
    DOI: 10.1002/(SICI)1099-0747(199709/12)13:3/43.0.CO;2-#
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