Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/329551
Type: Artigo
Title: Heavy Tailed Calibration Model With Berkson Measurement Errors For Replicated Data
Author: Blas
Betsabe; Bolfarine
Heleno; Lachos
Victor H.
Abstract: This work considers the so called controlled calibration model in which the independent variable is a controlled variable (Berkson type) and assumes that the measurement errors follow a scale mixtures of normal (SMN) distribution. The SMN family of distributions is an attractive class of symmetric distributions including the normal, Student-t, slash and contaminated normal distributions as special cases, providing a robust alternative to estimation in controlled calibration models in the absence of normality. An EM-type algorithm is developed, which is used to develop the local influence approach to assess the robustness aspects of the parameter estimates under four perturbation schemes. Results obtained from a real dataset in the area of chemistry are reported. (C) 2016 Elsevier B.V. All rights reserved.
Subject: Mc-em Algorithm
Scale Mixtures Of Normal Distributions
Controlled Variable
Calibration Model
Local Influence
Editor: Elsevier Science BV
Amsterdam
Rights: fechado
Identifier DOI: 10.1016/j.chemolab.2016.04.014
Address: http://www-sciencedirect-com.ez88.periodicos.capes.gov.br/science/article/pii/S0169743916301009?via%3Dihub
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File SizeFormat 
000380415100004.pdf1.13 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.