Functional data analysis with application to periodically stimulated foetal heart rate data. I: Functional regression

Sarah J. Ratcliffe*, Leo R. Leader, Gillian Z. Heller

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    33 Citations (Scopus)

    Abstract

    Functional regression is used to model longitudinal data where the number of measurements on the functional covariate is much greater than the number of subjects in the study. Thus, functional regression can be thought of as singular longitudinal analysis. We have modified existing functional regression techniques to the case of a functional covariate with a repeated stimulus. We applied this modified functional regression to periodically stimulated foetal heart rates. The heart rate tracings were used as a predictor of the child's psychomotor development at approximately 18 months of age. In the past, this type of data has been analysed using the partially subjective concept of habituation. By using the entire heart rate tracings through functional regression, we have the advantage that habituation does not need to be defined and all available information is used to predict later child development.

    Original languageEnglish
    Pages (from-to)1103-1114
    Number of pages12
    JournalStatistics in Medicine
    Volume21
    Issue number8
    DOIs
    Publication statusPublished - 30 Apr 2002

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