Improving prediction performance of stellar parameters using functional models

Sylvain Robbiano, Matthieu Saumard and Michel Curé

Journal of Applied Statistics, 2016, vol. 43, issue 8, 1465-1476

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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:43:y:2016:i:8:p:1465-1476

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DOI: 10.1080/02664763.2015.1106448

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