LEO-Py: Estimating likelihoods for correlated, censored, and uncertain data with given marginal distributions

Abstract

Data with uncertain, missing, censored, and correlated values are commonplace in many research fields including astronomy. Unfortunately, such data are often treated in an ad hoc way in the astronomical literature potentially resulting in inconsistent parameter estimates. Furthermore, in a realistic setting, the variables of interest or their errors may have non-normal distributions which complicates the modeling. I present a novel approach to compute the likelihood function for such data sets. This approach employs Gaussian copulas to decouple the correlation structure of variables and their marginal distributions resulting in a flexible method to compute likelihood functions of data in the presence of measurement uncertainty, censoring, and missing data. I demonstrate its use by determining the slope and intrinsic scatter of the star forming sequence of nearby galaxies from observational data. The outlined algorithm is implemented as the flexible, easy-to-use, open-source Python package LEO-Py.

21 pages, 8 figures, 2 tables, to appear in Astronomy and Computing, LEO-Py is available at github.com/rfeldmann/leopy

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• Some Aspects of Measurement Error in Linear Regression of Astronomical Data
• Constraining the Low-Mass Slope of the Star Formation Sequence at 0.5<z<2.5
• The star formation main sequence and stellar mass assembly of galaxies in the Illustris simulation
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• xGASS: Gas-rich central galaxies in small groups and their connections to cosmic web gas feeding
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Cited by in corpus (9)

• The cold interstellar medium of galaxies in the Local Universe
• Color gradients along the quiescent galaxy sequence: clues to quenching and structural growth
• The ALPINE-ALMA [CII] survey: Dust mass budget in the early Universe
• The Molecular-Gas Main Sequence and Schmidt-Kennicutt relation are fundamental, the Star-Forming Main Sequence is a (useful) byproduct
• r-process Abundance Patterns in the Globular Cluster M92
• Bayesian hierarchical modelling of the ${\mathrm{M}}_{\star }$-SFR relation from 1<z<6 in ASTRODEEP
• The slope and scatter of the star forming main sequence at z~5 : reconciling observations with simulations
• Regression for Astronomical Data with Realistic Distributions, Errors and Non-linearity
• The cold gas and dust properties of red star-forming galaxies