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Diagnostics for Additive Spline Models

In: Computing Science and Statistics

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  • Chong Gu

    (Purdue University, Department of Statistics)

Abstract

Recent developments of multivariate smoothing methods provide a rich collection of feasible models for non-parametric multivariate data analysis. Among the most popular are additive models. Here we interpret additive models as $$f(t)=\sum_{i=0}^{p}f_i(t)$$ where the fi’s are individually interpretable but not necessarily univariate functions. Definitions of various models and algorithms for computing the models have been the main concern of existing literature in this area. Few results are available on the validation of computed fits, and many applications of nonparametric methods unfortunately end up interpreting the noise. In this article I propose and illustrate some simple geometric diagnostics to help data analysts in detecting possible aliasing effects in computed nonparametric fits and in building parsimonious models in an interactive fashion. For their ready availability, interaction splines are used in the illustrations.

Suggested Citation

  • Chong Gu, 1992. "Diagnostics for Additive Spline Models," Springer Books, in: Connie Page & Raoul LePage (ed.), Computing Science and Statistics, pages 137-142, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4612-2856-1_17
    DOI: 10.1007/978-1-4612-2856-1_17
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