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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.)

Converting high-dimensional regression to high-dimensional conditional density estimation

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Autor(es):
Izbicki, Rafael [1] ; Lee, Ann B. [2]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Univ Fed Sao Carlos, Dept Stat, Sao Carlos, SP - Brazil
[2] Carnegie Mellon Univ, Dept Stat, Pittsburgh, PA 15213 - USA
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: ELECTRONIC JOURNAL OF STATISTICS; v. 11, n. 2, p. 2800-2831, 2017.
Citações Web of Science: 2
Resumo

There is a growing demand for nonparametric conditional density estimators (CDEs) in fields such as astronomy and economics. In astronomy, for example, one can dramatically improve estimates of the parameters that dictate the evolution of the Universe by working with full conditional densities instead of regression (i.e., conditional mean) estimates. More generally, standard regression falls short in any prediction problem where the distribution of the response is more complex with multi-modality, asymmetry or heteroscedastic noise. Nevertheless, much of the work on high-dimensional inference concerns regression and classification only, whereas research on density estimation has lagged behind. Here we propose FlexCode, a fully nonparametric approach to conditional density estimation that reformulates CDE as a non-parametric orthogonal series problem where the expansion coefficients are estimated by regression. By taking such an approach, one can efficiently estimate conditional densities and not just expectations in high dimensions by drawing upon the success in high-dimensional regression. Depending on the choice of regression procedure, our method can adapt to a variety of challenging high-dimensional settings with different structures in the data (e.g., a large number of irrelevant components and nonlinear manifold structure) as well as different data types (e.g., functional data, mixed data types and sample sets). We study the theoretical and empirical performance of our proposed method, and we compare our approach with traditional conditional density estimators on simulated as well as real-world data, such as photometric galaxy data, Twitter data, and line-of-sight velocities in a galaxy cluster. (AU)

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