Monte Carlo simulation studies in log-symmetric regressions
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2018-03-09
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Universidade Federal de Goiás
Resumo
This work deals with two Monte Carlo simulation studies in log-symmetric regression models,
which are particularly useful for the cases when the response variable is continuous, strictly
positive and asymmetric, with the possibility of the existence of atypical observations. In log-
symmetric regression models, the distribution of the random errors multiplicative belongs to
the log-symmetric class, which encompasses log-normal, log- Student-t, log-power-
exponential, log-slash, log-hyperbolic distributions, among others. The first simulation study
has as objective to examine the performance for the maximum-likelihood estimators of the
model parameters, where various scenarios are considered. The objective of the second
simulation study is to investigate the accuracy of popular information criteria as AIC, BIC,
HQIC and their respective corrected versions. As illustration, a movie data set obtained and
assembled for this dissertation is analyzed to compare log-symmetric models with the normal
linear model and to obtain the best model by using the mentioned information criteria.
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VENTURA, M. S. Monte Carlo simulation studies in log-symmetric regressions. 2018. 42 f. Dissertação (Mestrado em Economia) - Universidade Federal de Goiás, Goiânia, 2018.