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| Author(s): |
Taciana Kisaki Oliveira Shimizu
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2018-12-10 |
| Examining board members: |
Francisco Louzada Neto;
Teresa Cristina Martins Dias;
Paulo Henrique Ferreira da Silva;
Adriano Kamimura Suzuki;
Afrânio Márcio Corrêa Vieira
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| Advisor: | Francisco Louzada Neto |
| Abstract | |
Compositional data consist of known vectors such as compositions whose components are positive and defined in the interval (0,1) representing proportions or fractions of a whole, where the sum of these components must be equal to one. Compositional data is present in different areas, such as in geology, ecology, economy, medicine, among many others. Thus, there is great interest in new modeling approaches for compositional data, mainly when there is an influence of covariates in this type of data. In this context, the main objective of this thesis is to address the new approach of regression models applied in compositional data. The main idea consists of developing a marked method by penalized regression, in particular the Lasso (least absolute shrinkage and selection operator), elastic net and Spike-and-Slab Lasso (SSL) for the estimation of parameters of the models. In particular, we envision developing this modeling for compositional data, when the number of explanatory variables exceeds the number of observations in the presence of large databases, and when there are constraints on the dependent variables and covariates. (AU) | |
| FAPESP's process: | 14/16147-3 - Methods of penalized regression for compositional data |
| Grantee: | Taciana Kisaki Oliveira Shimizu |
| Support Opportunities: | Scholarships in Brazil - Doctorate |