Removing systematic errors for exoplanet search via latent causes

Removing systematic errors for exoplanet search via latent causes
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通过潜在原因消除系外行星搜索的系统误差

DOI:
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发表时间:
2015
期刊:
International Conference on Machine Learning
影响因子:
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通讯作者:
J. Peters
J. Peters
中科院分区:
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文献类型:
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作者:
B. Scholkopf;D. Hogg;Dun Wang;D. Foreman;D. Janzing;Carl;J. Peters

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我们描述了一种消除混杂因素影响的方法,以便重建潜在的感兴趣量。该方法被称为半兄弟回归,其灵感来自最近使用加性噪声模型进行因果推理的工作。我们提供了理论依据,并说明了该方法在具有挑战性的天文学应用中的潜力。
We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as half-sibling regression, is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification and illustrate the potential of the method in a challenging astronomy application.
DOI: 10.1093/biostatistics/kxj037
发表时间: 2007-01-01
期刊: BIOSTATISTICS
影响因子: 2.1
作者:
Johnson, W. Evan;Li, Cheng;Rabinovic, Ariel
通讯作者: Rabinovic, Ariel