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Model selection in high dimensions: theoretical properties and applications


The main goal of this research project is to study model selection methods for high-dimensional data analysis. The data will be composed by different types and structures, such as graphs and random networks, large vectors or temporal space data. The focus of this project is related to predictive models and the objective is to select models with adequate size in order to minimize the prediction error.This approach is related to the theory of statistical learning, which supports many of the techniques currently used in the fields of machine learning or data science. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
LEONARDI, FLORENCIA; LOPEZ-ROSENFELD, MATIAS; RODRIGUEZ, DANIELA; SEVERINO, MAGNO T. F.; SUED, MARIELA. Independent block identification in multivariate time series. JOURNAL OF TIME SERIES ANALYSIS, AUG 2020. Web of Science Citations: 0.
CERQUEIRA, ANDRESSA; GARIVIER, AURELIEN; LEONARDI, FLORENCIA. A note on perfect simulation for Exponential Random Graph Models. ESAIM-PROBABILITY AND STATISTICS, v. 24, p. 138-147, MAR 3 2020. Web of Science Citations: 0.

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