On testing inference in beta regressions
This article deals with testing inference in the class of beta regression models with varying dispersion. We focus on inference in small samples. We perform a numerical analysis in order to evaluate the sizes and powers of different tests. We consider the likelihood ratio test, two
adjusted likelihood ratio tests proposed by Ferrari and Pinheiro [Improved likelihood inference in beta regression, J. Stat. Comput. Simul. 81 (2011), pp. 431–443], the score test, the Wald test and bootstrap versions of the likelihood ratio, score and Wald tests. We perform tests
on the parameters that index the mean submodel and also on the parameters in the linear predictor of the precision submodel. Overall, the numerical evidence favours the bootstrap tests. It is also shown that the score test is considerably less size-distorted than the likelihood ratio and Wald
tests. An application that uses real (not simulated) data is presented and discussed.
Keywords: Wald test; beta regression; bootstrap; likelihood ratio test; profile likelihood; score test
Document Type: Research Article
Affiliations: Departamento de Estatística, Universidade Federal de Pernambuco, Cidade Universitária, Recife/PE, 50740–540, Brazil
Publication date: 02 January 2014
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