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  • Combining non-parametric models with logistic regression: an application to motor vehicle injury data.

    Author(s)
    Kuhnert, P
    Do, Kim Anh
    McClure, Roderick John
    Griffith University Author(s)
    McClure, Roderick J.
    Year published
    2000
    Metadata
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    Abstract
    To date, computer-intensive non-parametric modelling procedures such as classification and regression trees (CART) and multivariate adaptive regression splines (MARS) have rarely been used in the analysis of epidemiological studies. Most published studies focus on techniques such as logistic regression to summarise their results simply in the form of odds ratios. However flexible, non-parametric techniques such as CART and MARS can provide more informative and attractive models whose individual components can be displayed graphically. An application of these sophisticated techniques in the analysis of an epidemiological ...
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    To date, computer-intensive non-parametric modelling procedures such as classification and regression trees (CART) and multivariate adaptive regression splines (MARS) have rarely been used in the analysis of epidemiological studies. Most published studies focus on techniques such as logistic regression to summarise their results simply in the form of odds ratios. However flexible, non-parametric techniques such as CART and MARS can provide more informative and attractive models whose individual components can be displayed graphically. An application of these sophisticated techniques in the analysis of an epidemiological case-control study of injuries resulting from motor vehicle accidents has been encouraging. They have not only identified potential areas of risk largely governed by age and number of years driving experience but can also identify outlier groups and can be used as a precursor to a more detailed logistic regression analysis.
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    Journal Title
    Computational Statistics and Data analysis
    Volume
    34
    Issue
    3
    DOI
    https://doi.org/10.1016/S0167-9473(99)00099-7
    Subject
    Statistics
    Computation Theory and Mathematics
    Econometrics
    Publication URI
    http://hdl.handle.net/10072/58341
    Collection
    • Journal articles

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