Properties of Transformation Quantile Regression Model
Year: 2013
Type: article
Abstract: We present in this paper a few important direction on research using quantile regression. \nWe start from some motivation for this method of regression. Secondly we present some \nmain areas of application this method. Finally we wanted to point out transformation of the main \nmodel. This model, introduced by Powell (1991) and further analyzed by Chamberlain (1994) and \nBuchinsky (1995), specifies the conditional quantiles of the Box-Cox transformation of the variable \nunder appraisal as a linear function of the covariates. It provides, within a simple set-up, the \nneeded flexibility, as both the transformation parameter and the coefficients of the linear function \nare allowed to vary freely at each point of the distribution. The Box-Cox quantile regression, \nwhich has the linear and log-linear models as particular cases, will provide, therefore, a direct \nanswer to the question of the appropriate transformation to be used.
Author: Grażyna Trzpiot
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W1573217421