Nonparametric Methods in Astronomy: Think, Regress, Observe—Pick Any Three. (15th January 2018)

• Record Type: Journal Article
• Authors: Steinhardt, Charles L.; Jermyn, Adam S.
• Is Part Of: Publications of the Astronomical Society of the Pacific. Volume 130:Number 984(2018)
• Publication Date: 2018-01-15
• Subjects: methods: analytical -- methods: data analysis -- methods: numerical -- methods: statistical; Astronomy -- Periodicals; Astronomy; Periodicals; Periodicals; 520.5
• DOI: 10.1088/1538-3873/aaa22a
• Languages: English
• ISSNs: 0004-6280

Abstract

Abstract: Telescopes are much more expensive than astronomers, so it is essential to minimize required sample sizes by using the most data-efficient statistical methods possible. However, the most commonly used model-independent techniques for finding the relationship between two variables in astronomy are flawed. In the worst case they can lead without warning to subtly yet catastrophically wrong results, and even in the best case they require more data than necessary. Unfortunately, there is no single best technique for nonparametric regression. Instead, we provide a guide for how astronomers can choose the best method for their specific problem and provide a python library with both wrappers for the most useful existing algorithms and implementations of two new algorithms developed here.