A Statistical Method for Estimating Luminosity Functions using Truncated Data

Astronomy and Astrophysics – Astrophysics

Scientific paper

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30 pages, 9 figures, Accepted for publication in ApJ

Scientific paper

10.1086/515390

The observational limitations of astronomical surveys lead to significant statistical inference challenges. One such challenge is the estimation of luminosity functions given redshift $z$ and absolute magnitude $M$ measurements from an irregularly truncated sample of objects. This is a bivariate density estimation problem; we develop here a statistically rigorous method which (1) does not assume a strict parametric form for the bivariate density; (2) does not assume independence between redshift and absolute magnitude (and hence allows evolution of the luminosity function with redshift); (3) does not require dividing the data into arbitrary bins; and (4) naturally incorporates a varying selection function. We accomplish this by decomposing the bivariate density into nonparametric and parametric portions. There is a simple way of estimating the integrated mean squared error of the estimator; smoothing parameters are selected to minimize this quantity. Results are presented from the analysis of a sample of quasars.

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