Selective inference for high-dimensional data

• Grant number: 24/18134-8
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: December 01, 2024
• End date: May 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Statistics
• Principal Investigator: Aluísio de Souza Pinheiro
• Grantee: Luan Portella da Silva
• Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 23/02538-0 - Time series, wavelets, high dimensional data and applications, AP.TEM

Abstract

Big data and artificial intelligence brings forward several notable technological advancements for decades to come. Assessing relevant information from large volume of data depends on fast and reliable algorithms. Two exampled here ar feature screening and statistical methods for the dynamics of serial data. The first task consists in discriminating which variables are noise, and which have statistical information. The second task performs estimation and testing regarding the temporal dynamics for serial data. The challenge in modern days is to perform well in these tasks embedded on a high dimensional/ big data framework. Our proposals del with these tasks from spectral paradigms (fast Fourier FFT and wavelets FWT).