Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/342368
Type: Artigo
Title: Wavelet-based estimators for mixture regression
Author: Montoril, Michel H.
Pinheiro, Aluisio
Vidakovic, Brani
Abstract: We consider a process that is observed as a mixture of two random distributions, where the mixing probability is an unknown function of time. The setup is built upon a wavelet-based mixture regression. Two linear wavelet estimators are proposed. Furthermore, we consider three regularizing procedures for each of the two wavelet methods. We also discuss regularity conditions under which the consistency of the wavelet methods is attained and derive rates of convergence for the proposed estimators. A Monte Carlo simulation study is conducted to illustrate the performance of the estimators. Various scenarios for the mixing probability function are used in the simulations, in addition to a range of sample sizes and resolution levels. We apply the proposed methods to a data set consisting of array Comparative Genomic Hybridization from glioblastoma cancer studies
Subject: Classificação
Country: Reino Unido
Editor: Wiley
Rights: Fechado
Identifier DOI: 10.1111/sjos.12344
Address: https://onlinelibrary.wiley.com/doi/full/10.1111/sjos.12344
Date Issue: 2019
Appears in Collections:IMECC - Artigos e Outros Documentos

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