Robbiano, S;
Saumard, M;
Curé, M;
(2016)
Improving prediction performance of stellar parameters using functional models.
Journal of Applied Statistics
, 43
(8)
pp. 1465-1476.
10.1080/02664763.2015.1106448.
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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.
| Type: | Article |
|---|---|
| Title: | Improving prediction performance of stellar parameters using functional models |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/02664763.2015.1106448 |
| Publisher version: | http://dx.doi.org/10.1080/02664763.2015.1106448 |
| Language: | English |
| Additional information: | This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 26 November 2015, available online: http://www.tandfonline.com/10.1080/02664763.2015.1106448. |
| Keywords: | functional data, spectra, astronomy, regression, prediction intervals |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1490982 |
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