Título: Wavelet Feature Screening 

Data: 28/08/2024

Palestrante: Rodney Fonseca (UFBA)

Local:  Laboratório de Sistemas Estocásticos (LSE), Sala I-044-B, Centro de Tecnologia - UFRJ

Resumo: An initial screening of which covariates are relevant is a common practice in high-dimensional regression models. The classic feature screening selects only a subset of covariates correlated with the response variable. However, many important features might have a relevant albeit highly nonlinear relation with the response. One screening approach that handles nonlinearity is to compute the correlation between the response and nonparametric functions of each covariate. Wavelets are powerful tools for nonparametric and functional data analysis but are still seldom used in the feature screening literature. In this talk, we introduce a wavelet feature screening method that can be easily implemented. Theoretical and simulation results show that the proposed method can capture true covariates with high probability, even in highly nonlinear models. We also present an example with real data in a high-dimensional setting. This is a joint work with Pedro Morettin and Aluísio Pinheiro.