Measurement errors and scaling relations in astrophysics: A review

S. Andreon, M. A. Hurn

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

This review article considers some of the most common methods used in astronomy for regressing one quantity against another in order to estimate the model parameters or to predict an observationally expensive quantity using trends between object values. These methods have to tackle some of the awkward features prevalent in astronomical data, namely heteroscedastic (point-dependent) errors, intrinsic scatter, non-ignorable data collection and selection effects, data structure and non-uniform population (often called Malmquist bias), non-Gaussian data, outliers and mixtures of regressions. We outline how least square fits, weighted least squares methods, Maximum Likelihood, survival analysis, and Bayesian methods have been applied in the astrophysics literature when one or more of these features is present. In particular we concentrate on errors-in-variables regression and we advocate Bayesian techniques.
Original languageEnglish
Pages (from-to)15-33
Number of pages19
JournalStatistical Analysis and Data Mining
Volume6
Issue number1
DOIs
Publication statusPublished - Jan 2013

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Astrophysics
Scaling Relations
Measurement errors
Measurement Error
Regression
Errors in Variables
Astronomy
Survival Analysis
Weighted Least Squares
Bayesian Methods
Scatter
Least Square Method
Maximum likelihood
Outlier
Maximum Likelihood
Data structures
Least Squares
Data Structures
Predict
Dependent

Cite this

Measurement errors and scaling relations in astrophysics : A review. / Andreon, S.; A. Hurn, M.

In: Statistical Analysis and Data Mining, Vol. 6, No. 1, 01.2013, p. 15-33.

Research output: Contribution to journalArticle

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