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Graphical Chain Models and their Application

In: Statistical Modelling and Regression Structures

Author

Listed:
  • Iris Pigeot

    (University of Bremen, Bremen Institute for Prevention Research and Social Medicine (BIPS))

  • Stephan Klasen

    (University of Göttingen, Department of Economics)

  • Ronja Foraita

    (University of Bremen, Bremen Institute for Prevention Research and Social Medicine (BIPS))

Abstract

Graphical models are a powerful tool to analyze multivariate data sets that allow to reveal direct and indirect relationships and to visualize the association structure in a graph. As with any statistical analysis, however, the obtained results partly reflect the uncertainty being inherent in any type of data and depend on the selected variables to be included in the analysis, the coding of these variables and the selection strategy used to fit the graphical models to the data. This paper suggests that these issues may be even more crucial for graphical models than for simple regression analyses due to the large number of variables considered which means that a fitted graphical model has to be interpreted with caution. Sensitivity analyses might be recommended to assess the stability of the obtained results. This will be illustrated using a data set on undernutrition in Benin.

Suggested Citation

  • Iris Pigeot & Stephan Klasen & Ronja Foraita, 2010. "Graphical Chain Models and their Application," Springer Books, in: Thomas Kneib & Gerhard Tutz (ed.), Statistical Modelling and Regression Structures, pages 231-247, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2413-1_13
    DOI: 10.1007/978-3-7908-2413-1_13
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