Measurement Error Models in Astronomy
Abstract
I discuss the effects of measurement error on regression and density estimation. I review the statistical methods that have been developed to correct for measurement error that are most popular in astronomical data analysis, discussing their advantages and disadvantages. I describe functional models for accounting for measurement error in regression, with emphasis on the methods of moments approach and the modified loss function approach. I then describe structural models for accounting for measurement error in regression and density estimation, with emphasis on maximum-likelihood and Bayesian methods. As an example of a Bayesian application, I analyze an astronomical data set subject to large measurement errors and a non-linear dependence between the response and covariate. I conclude with some directions for future research.
Cite
@article{arxiv.1112.1745,
title = {Measurement Error Models in Astronomy},
author = {Brandon C. Kelly},
journal= {arXiv preprint arXiv:1112.1745},
year = {2011}
}
Comments
15 pages, 2 figures, review based on invited talk at "Statistical Challenges in Modern Astronomy V", held at Penn State in June 2011, to appear in the proceedings