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Improving prediction performance of stellar parameters using functional models

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  • Sylvain Robbiano
  • Matthieu Saumard
  • Michel Curé

Abstract

This paper investigates the problem of prediction of stellar parameters, based on the star's electromagnetic spectrum. The knowledge of these parameters permits to infer on the evolutionary state of the star. From a statistical point of view, the spectra of different stars can be represented as functional data. Therefore, a two-step procedure decomposing the spectra in a functional basis combined with a regression method of prediction is proposed. We also use a bootstrap methodology to build prediction intervals for the stellar parameters. A practical application is also provided to illustrate the numerical performance of our approach.

Suggested Citation

  • Sylvain Robbiano & Matthieu Saumard & Michel Curé, 2016. "Improving prediction performance of stellar parameters using functional models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1465-1476, June.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1465-1476
    DOI: 10.1080/02664763.2015.1106448
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