L<sub>1</sub>-regularization for Generalized Linear Models using Quantile Universal Threshold

Diplôme Master L<sub>1</sub>-regularization for Generalized Linear Models using Quantile Universal Threshold eng 2014 author Diaz Jairo 782051 director Sardy Sylvain 629017 Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data, and most of the time, they are in the situation of processing data whose generative source is uncertain. Several models and techniques have been developed to satisfy the necessities of this huge task, and most of those techniques need to define different parameters that should be chosen for the good behavior of estimation or classification of future data. In this thesis we focus in the important, and highly used L1-regularization, and how to choose its corresponding regularization parameter for Generalized Linear Models (GLM) by using a Quantile Universal Threshold (QUT) method, and in this way we show an automatic method to obtain the vector of coefficients and therefore, to be able to perform estimation and model selection in GLM. We show some experimental results that corroborate the capacity of QUT to include the true models in simulated data, and some results with real data. Regularization Generalized linear models Variance estimation Phase transition Prediction Dewey 510 Mathematiques et Sciences Informatiques 25 Section de mathématiques 11 false 852 Probabilités et statistique d4d9cc5b-177c-4ec9-b9c0-518eed4753e6 Master thesis Restricted thesisV4_article.pdf 1099305 application/pdf restrictedAccess Faculté des sciences / Section de mathématiques

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L₁-regularization for Generalized Linear Models using Quantile Universal Threshold				2014	345	11

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Diaz, Jairo