Measurement errors and scaling relations in astrophysics:A review


Andreon, S. and A. Hurn, M., 2013. Measurement errors and scaling relations in astrophysics:A review. Statistical Analysis and Data Mining, 6 (1), pp. 15-33.

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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.


Item Type Articles
CreatorsAndreon, S.and A. Hurn, M.
Related URLs
DepartmentsFaculty of Science > Mathematical Sciences
Research CentresBath Institute for Complex Systems (BICS)
ID Code32334
Additional InformationInvited review.


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