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Nonparametric Methods in Astronomy: Think, Regress, Observe—Pick Any Three

Steinhardt, Charles L. and Jermyn, Adam S. (2018) Nonparametric Methods in Astronomy: Think, Regress, Observe—Pick Any Three. Publications of the Astronomical Society of the Pacific, 130 (984). Art. No. 023001. ISSN 0004-6280. https://resolver.caltech.edu/CaltechAUTHORS:20180118-133730015

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/1538-3873/aaa22aDOIArticle
http://iopscience.iop.org/article/10.1088/1538-3873/aaa22a/metaPublisherArticle
https://arxiv.org/abs/1801.06545arXivDiscussion Paper
ORCID:
AuthorORCID
Steinhardt, Charles L.0000-0003-3780-6801
Jermyn, Adam S.0000-0001-5048-9973
Additional Information: © 2018. The Astronomical Society of the Pacific. Received 2017 August 22; accepted 2017 December 14; published 2018 January 15. The authors thank Richard Yi for several helpful conversations in developing these ideas and the anonymous referee for a review process that substantially improved this work. The authors would also like to thank Jogesh Babu, Douglas Boubert, Peter Capak, Jens Hjorth, Nick Lee, Dan Masters, Josh Speagle, and Sune Toft for helpful comments. C.S. acknowledges support from the ERC Consolidator Grant funding scheme (project ConTExt, grant number No. 648179) and from the Carlsberg Foundation. A.S.J. is supported by a Marshall scholarship.
Group:Infrared Processing and Analysis Center (IPAC)
Funders:
Funding AgencyGrant Number
European Research Council (ERC)648179
Carlsberg FoundationUNSPECIFIED
Marshall FoundationUNSPECIFIED
Subject Keywords:methods: analytical – methods: data analysis – methods: numerical – methods: statistical
Issue or Number:984
Record Number:CaltechAUTHORS:20180118-133730015
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180118-133730015
Official Citation:Charles L. Steinhardt and Adam S. Jermyn 2018 PASP 130 023001
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:84386
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:18 Jan 2018 22:33
Last Modified:26 Nov 2019 20:00

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