![\"\"](\"https:\/\/www.ufrgs.br\/ppgest\/wp-content\/uploads\/2024\/11\/Banner_Rodney-1024x1024.jpeg\")
<\/p>\n<\/p>\n
Temos o prazer de convidar a todos para mais um semin\u00e1rio do Programa de P\u00f3s-Gradua\u00e7\u00e3o em Estat\u00edstica da UFRGS (PPGEst-UFRGS).<\/p>\n
Seguem os detalhes do semin\u00e1rio:<\/p>\n
Palestrante:<\/strong> Prof. Dr. Rodney Fonseca (UFBA)<\/p>\n T\u00edtulo:<\/strong> Wavelet Feature Screening<\/p>\n 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. Joint work with Pedro Morettin and Alu\u00edsio Pinheiro.<\/p>\n Data:<\/strong> 05\/11\/24<\/p>\n Hor\u00e1rio:\u00a0<\/strong>13h30min.<\/p>\n