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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

PHOTO-z ESTIMATION: AN EXAMPLE OF NONPARAMETRIC CONDITIONAL DENSITY ESTIMATION UNDER SELECTION BIAS

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Autor(es):
Izbicki, Rafael ; Lee, Ann B. ; Freeman, Peter E.
Número total de Autores: 3
Tipo de documento: Artigo Científico
Fonte: Annals of Applied Statistics; v. 11, n. 2, p. 698-724, JUN 2017.
Citações Web of Science: 1
Resumo

Redshift is a key quantity for inferring cosmological model parameters. In photometric redshift estimation, cosmologists use the coarse data collected from the vast majority of galaxies to predict the redshift of individual galaxies. To properly quantify the uncertainty in the predictions, however, one needs to go beyond standard regression and instead estimate the full conditional density f (z|x) of a galaxy's redshift z given its photometric covariates x. The problem is further complicated by selection bias: usually only the rarest and brightest galaxies have known redshifts, and these galaxies have characteristics and measured covariates that do not necessarily match those of more numerous and dimmer galaxies of unknown redshift. Unfortunately, there is not much research on how to best estimate complex multivariate densities in such settings. Here we describe a general framework for properly constructing and assessing nonparametric conditional density estimators under selection bias, and for combining two or more estimators for optimal performance. We propose new improved photo-z estimators and illustrate our methods on data from the Sloan Data Sky Survey and an application to galaxy-galaxy lensing. Although our main application is photo-z estimation, our methods are relevant to any high-dimensional regression setting with complicated asymmetric and multimodal distributions in the response variable. (AU)

Processo FAPESP: 17/03363-8 - Interpretabilidade e eficiência em testes de hipótese
Beneficiário:Rafael Izbicki
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 14/25302-2 - Uma abordagem flexível para a estimação de uma densidade condicional em problemas com alta dimensionalidade
Beneficiário:Rafael Izbicki
Modalidade de apoio: Auxílio à Pesquisa - Regular