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Methods of penalized regression for compositional data

Grant number: 14/16147-3
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): August 01, 2014
Effective date (End): July 31, 2018
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Applied Probability and Statistics
Principal researcher:Francisco Louzada Neto
Grantee:Taciana Kisaki Oliveira Shimizu
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

Compositional data consist of known vectors like 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, as in geology, ecology, economy, medicine, among many others. In this way, there is a great interest in new modeling approaches for compositional data, principally when there is influence of covariates in this type of data. In this context, the main objective of this project is to propose the new approach of regression models applied in compositional data. The basic idea consists to develop of a marked method by penalized regression, in particular the Lasso (least absolute shrinkage and selection operator), for the estimation of parameters via classical and Bayesian inference. In particular, we envision developing this modeling for compositional data, with the number of explanatory variables exceeds the number of observations in the presence of large databases, and furthermore, when there is presence of components equal to zero. (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)
LOUZADA, FRANCISCO; SHIMIZU, TACIANA K. O.; SUZUKI, ADRIANO K. The Spike-and-Slab Lasso regression modeling with compositional covariates: An application on Brazilian children malnutrition data. STATISTICAL METHODS IN MEDICAL RESEARCH, v. 29, n. 5 JULY 2019. Web of Science Citations: 1.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.