Regression models in functional data analysis

• Grant number: 13/09035-1
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: August 01, 2013
• End date: August 31, 2015
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics - Statistics
• Principal Investigator: Aluísio de Souza Pinheiro
• Grantee: Michel Helcias Montoril
• Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 13/00506-1 - Time series, wavelets and functional data analysis, AP.TEM
• Associated scholarship(s): 13/21273-5 - Estimation of semi-functional linear models by wavelets, BE.EP.PD

Abstract

The technological advances have helped statistics to develop studies in many fields. We can highlight the functional data analysis, in which each data point is related to continuous process. From mathematical tools, e.g., kernels, splines, wavelets and so on, inferences can be performed by functional models. In this project we are interested in studying functional regression models, due to their diversified application, showing their important role in several fields. These models have functions that are unknown and have practical interest. The aim in this research concerns the study of such functions by wavelets, which up the present moment has not been performed. Based on this, we believe that it is possible to develop theoretical and numerical results. We also want to compare our results to others already known in literature, in order to evaluate in which situations our proposal performs better. We intend to fulfill the numerical results by using the R program. The results of the research will be submitted to journals in the related areas.

Scientific publications

(5)

(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)

MONTORIL, MICHEL H.; PINHEIRO, ALUISIO; VIDAKOVIC, BRANI. Wavelet-based estimators for mixture regression. SCANDINAVIAN JOURNAL OF STATISTICS, v. 46, n. 1, p. 215-234, MAR 2019. (13/09035-1, 13/21273-5, 13/00506-1)

MONTORIL, MICHEL H.; MORETTIN, PEDRO A.; CHIANN, CHANG. Wavelet estimation of functional coefficient regression models. INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING, v. 16, n. 1, JAN 2018. (13/21273-5, 13/09035-1, 08/51097-6, 13/00506-1, 09/09588-5)

MONTORIL, MICHEL H.; MORETTIN, PEDRO A.; CHIANN, CHANG. Spline estimation of functional coefficient regression models for time series with correlated errors. Statistics & Probability Letters, v. 92, p. 226-231, SEP 2014. (08/51097-6, 13/09035-1, 09/09588-5)

FARIAS, RAFAEL B. A.; MONTORIL, MICHEL H.; ANDRADE, JOSE A. A.. Bayesian inference for extreme quantiles of heavy tailed distributions. Statistics & Probability Letters, v. 113, p. 103-107, JUN 2016. (13/09035-1)

MONTORIL, MICHEL H.; CHANG, WOOJIN; VIDAKOVIC, BRANI. Wavelet-Based Estimation of Generalized Discriminant Functions. SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS, v. 81, n. 2, p. 318-349, DEC 2019. (13/09035-1, 13/21273-5)