Bartlett and Bartlett-type corrections in heteroscedastic symmetric nonlinear regression models

Abstract This paper provides general expressions for Bartlett and Bartlett-type correction factors for the likelihood ratio and gradient statistics to test the dispersion parameter vector in heteroscedastic symmetric nonlinear models. This class of regression models is potentially useful to model da...

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Main Authors: MARIANA C. ARAÚJO, AUDREY H.M.A. CYSNEIROS, MONTENEGRO. CYSNEIROS
格式: 文件
语言:English
出版: Academia Brasileira de Ciências 2022-11-01
丛编:Anais da Academia Brasileira de Ciências
主题:
在线阅读:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000700302&lng=en&tlng=en
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author MARIANA C. ARAÚJO
AUDREY H.M.A. CYSNEIROS
MONTENEGRO. CYSNEIROS
author_facet MARIANA C. ARAÚJO
AUDREY H.M.A. CYSNEIROS
MONTENEGRO. CYSNEIROS
author_sort MARIANA C. ARAÚJO
collection DOAJ
description Abstract This paper provides general expressions for Bartlett and Bartlett-type correction factors for the likelihood ratio and gradient statistics to test the dispersion parameter vector in heteroscedastic symmetric nonlinear models. This class of regression models is potentially useful to model data containing outlying observations. Furthermore, we develop Monte Carlo simulations to compare size and power of the proposed corrected tests to the original likelihood ratio, score, gradient tests, corrected score test, and bootstrap tests. Our simulation results favor the score and gradient corrected tests as well as the bootstrap tests. We also present an empirical application.
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spelling doaj.art-f8ab96cec0bc43b684a93f9b51a7710a2022-12-22T02:46:42ZengAcademia Brasileira de CiênciasAnais da Academia Brasileira de Ciências1678-26902022-11-0194suppl 310.1590/0001-3765202220200568Bartlett and Bartlett-type corrections in heteroscedastic symmetric nonlinear regression modelsMARIANA C. ARAÚJOhttps://orcid.org/0000-0002-5682-520XAUDREY H.M.A. CYSNEIROShttps://orcid.org/0000-0002-8920-4004MONTENEGRO. CYSNEIROShttps://orcid.org/0000-0003-0867-3931Abstract This paper provides general expressions for Bartlett and Bartlett-type correction factors for the likelihood ratio and gradient statistics to test the dispersion parameter vector in heteroscedastic symmetric nonlinear models. This class of regression models is potentially useful to model data containing outlying observations. Furthermore, we develop Monte Carlo simulations to compare size and power of the proposed corrected tests to the original likelihood ratio, score, gradient tests, corrected score test, and bootstrap tests. Our simulation results favor the score and gradient corrected tests as well as the bootstrap tests. We also present an empirical application.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000700302&lng=en&tlng=enBartlett correctionsBartlett-type correctionsbootstrapgradient testlarge-sample test statistics
spellingShingle MARIANA C. ARAÚJO
AUDREY H.M.A. CYSNEIROS
MONTENEGRO. CYSNEIROS
Bartlett and Bartlett-type corrections in heteroscedastic symmetric nonlinear regression models
Anais da Academia Brasileira de Ciências
Bartlett corrections
Bartlett-type corrections
bootstrap
gradient test
large-sample test statistics
title Bartlett and Bartlett-type corrections in heteroscedastic symmetric nonlinear regression models
title_full Bartlett and Bartlett-type corrections in heteroscedastic symmetric nonlinear regression models
title_fullStr Bartlett and Bartlett-type corrections in heteroscedastic symmetric nonlinear regression models
title_full_unstemmed Bartlett and Bartlett-type corrections in heteroscedastic symmetric nonlinear regression models
title_short Bartlett and Bartlett-type corrections in heteroscedastic symmetric nonlinear regression models
title_sort bartlett and bartlett type corrections in heteroscedastic symmetric nonlinear regression models
topic Bartlett corrections
Bartlett-type corrections
bootstrap
gradient test
large-sample test statistics
url http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000700302&lng=en&tlng=en
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