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arXiv · 1706.10121

Sliced Inverse Regression for the inference of stellar fundamental parameters

V. Watson·JF. Trouilhet·F. Paletou·M. Gebran
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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.

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V. Watson, JF. Trouilhet, F. Paletou, M. Gebran. 2017-06-30. Sliced Inverse Regression for the inference of stellar fundamental parameters. https://arxiv.org/abs/1706.10121

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