RESUMO
The Beta distribution is the standard model for quantifying the influence of covariates on the mean of a response variable on the unit interval. However, this well-known distribution is no longer useful when we are interested in quantifying the influence of such covariates on the quantiles of the response variable. Unlike Beta, the Kumaraswamy distribution has a closed-form expression for its quantile and can be useful for the modeling of quantiles in the absence/presence of covariates. As an alternative to the Kumaraswamy distribution for the modeling of quantiles, in this paper the unit-Weibull distribution was considered. This distribution was obtained by the transformation of a random variable with Weibull distribution. The same transformation applied to a random variable with Exponentiated Exponential distribution generates the Kumaraswamy distribution. The suitability of our proposal was demonstrated to model quantiles, conditional on covariates, with two simulated examples and three real applications with datasets from health, accounting and social science. For such data sets, the obtained fits of the proposed regression model were compared with those provided by the Beta and Kumaraswamy regression models.
RESUMO
In a note about the paper titled 'On the one parameter unit-Lindley distribution and its associated regression model for proportion data', Mazucheli et al. [J. Appl. Stat. 46 (2019), pp. 700-714] and Nadarajah and Chan [On moments of the unit Lindley distribution, J. Appl. Stat. (under review)] claim that 'The expressions given for the moments and incomplete moments are not correct and not in closed form'. While we agree that they are not in closed form and observe a typo in the expressions for µ k ' and T k ( t ) , k = 1 , , the expressions for µ 1 ' , µ 2 ' , µ 3 ' and µ 4 ' are, however, entirely correct.
