Estimação de funções do redshift de galáxias com base em dados fotométricos
Ferreira, Gretta Rossi
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In a substantial amount of astronomy problems, we are interested in estimating values assumed of some unknown quantity z ∈ R, for many function g, based on covariates x ∈ R^d. This is made using a sample (X1,Z1), ... ,(Xn,Zn). Two approaches that are usually used to solve this problem consist in (1) estimating a regression function of Z in x and plugging it into the g or (2) estimating a conditional density f(z|x) and plugging it into integral of g(z)f(z|x)dz. Unfortunately, few studies exhibit quantitative comparisons between these two approaches. Besides that, few conditional density estimation methods had their performance compared in these problems. In view of this, the objective of this work is to show several comparisons of techniques used to estimate functions of unknown quantity. In particular we highlight nonparametric methods. In addition to estimators (1) and (2), we also propose a new approach that consists in directly estimating the regression function from g (Z) on x . These approaches were tested in different functions in the DEEP2 and Sheldon 2012 datasets. For almost all the functions tested, the estimator (1) obtained the worst results, except when we use the random forests methods. In several cases, the proposed new approach presented better results, as well as the estimator (2). In particular, we verified that random forests methods generally present to good results.