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Mixed-Effects Multivariate Adaptive Splines Models

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Nonlinear Estimation and Classification

Part of the book series: Lecture Notes in Statistics ((LNS,volume 171))

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Summary

A mixed-effects multivariate adaptive splines model is presented for analyzing longitudinal or growth curves data that may or may not have been collected through a regular measurement schedule. The MASAL (an acronym for multivariate adaptive splines for the analysis of longitudinal data) algorithm by Zhang [19, 20, 21] is used to determine the nonparametric fixed-effects in the mixed-effects multivariate adaptive splines model. The original MASAL algorithm requires the characterization and specification of the within subject autocorrelation structure, which is usually a tedious while not always rewarding process. In contrast, the idea of mixed-effects is introduced to the MASAL algorithm in this work, leading to an automated procedure for analysis of longitudinal and growth curves data. To demonstrate the great potential of this new procedure, I re-analyzed a data set on the effect of cocaine use by pregnant women on the growth of their infants after birth. The numerical results are remarkable as opposed to a previously published analysis by [21] in terms of the dissection of random effects.

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Author information

Authors and Affiliations

  1. Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, 06520, USA

    Heping Zhang

Authors
  1. Heping Zhang
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Imperial College, 180 Queen’s Gate, London, SW7 2BZ, UK

    David D. Denison  & Christopher C. Holmes  & 

  2. Room 2C283 Bell Laboratories, Lucent Technologies, 600 Mountain Avenue, Murray Hill, NJ, 07974-0636, USA

    Mark H. Hansen

  3. Statistical Department, Texas A&M University, College Station, TX, 77843-3143, USA

    Bani Mallick

  4. Department of Statistics, University of California, Berkeley, Berkeley, CA, 94720-3860, USA

    Bin Yu

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Zhang, H. (2003). Mixed-Effects Multivariate Adaptive Splines Models. In: Denison, D.D., Hansen, M.H., Holmes, C.C., Mallick, B., Yu, B. (eds) Nonlinear Estimation and Classification. Lecture Notes in Statistics, vol 171. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21579-2_18

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  • DOI: https://doi.org/10.1007/978-0-387-21579-2_18

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-95471-4

  • Online ISBN: 978-0-387-21579-2

  • eBook Packages: Springer Book Archive

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