Sliced Inverse Regression for the inference of stellar fundamental parameters
Instrumentation and Methods for Astrophysics
2017-07-03 v1 Solar and Stellar Astrophysics
Abstract
We aim at finding the value of an explanatory variable, through its expression in a large data-vector, without knowing the link function between the explanatory variable and the data-space. Sliced Inverse Regression (SIR) method allows for the projection of a data-vector onto a subspace consistent with the explanatory variable variation. We suggest a method based on the SIR subspace, that gives the most efficient estimation of an unknown explanatory variable.
Keywords
Cite
@article{arxiv.1706.10121,
title = {Sliced Inverse Regression for the inference of stellar fundamental parameters},
author = {V. Watson and JF. Trouilhet and F. Paletou and M. Gebran},
journal= {arXiv preprint arXiv:1706.10121},
year = {2017}
}
Comments
in French. to appear in: http://www.gretsi.fr/ XXVI-th colloquium proc. (text in french; maths in maths)