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

Kiros Berhane, Jonnagadda S Rao

Research output: Contribution to journalArticle

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
Pages (from-to)289-296
Number of pages8
JournalApplied Stochastic Models and Data Analysis
Volume13
Issue number3-4
StatePublished - Sep 1 1997
Externally publishedYes

Fingerprint

Parameter Selection
Smoothing Parameter
Nonparametric Model
Longitudinal Data
Generalized Cross-validation
Correlated Data
Attack
Nonparametric model
Smoothing
Longitudinal data
Cross-validation
Model

Keywords

  • BRUTO
  • 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

Automatic smoothing parameter selection in non-parametric models for longitudinal data. / Berhane, Kiros; Rao, Jonnagadda S.

In: Applied Stochastic Models and Data Analysis, Vol. 13, No. 3-4, 01.09.1997, p. 289-296.

Research output: Contribution to journalArticle

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