The Box-Cox quantile regression model using the two stage method introduced by Chamberlain (1994) and Buchinsky (1995) provides an attractive extension of linear quantile regression techniques. However, a major numerical problem exists when implementing this method which has not been addressed so far in the literature. We suggest a simple solution modifying the estimator slightly. This modification is easy to implement. The modified estimator is still n–consistent and its asymptotic distribution can easily be derived. A simulation study confirms that the modified estimator works well
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
We propose a corrected plug-in method for constructing confidence intervals of the conditional quant...
Koenker & Basset, 1978 introduce the quantile regression estimator, that allows to have a more compl...
The Box-Cox quantile regression model using the two stage method introduced by Chamberlain (1994) an...
The Box-Cox quantile regression model using the two stage method introduced by Chamberlain (1994) an...
We present in this paper a few important direction on research using quantile regression. We start ...
The Box–Cox quantile regression model introduced by Powell (1991) is a flexible and numerically attr...
This paper presents an alternative approach to the likelihood methods for estimating the parameter A...
This paper presents an alternative approach to the likelihood methods for estimating the parameter A...
The Box-Cox method has been widely used to improve estimation accuracy in different fields, especial...
Powell (1986) proposed a quantile regression estimator for censored regression models on the basis o...
Modelling the quantiles of a random variable is facilitated by their equivariance to monotone transf...
A quantile-based method for estimating the Box-Cox transformation in random samples is presented. Th...
Transformation of a response variable can greatly expand the class of problems for which the linear ...
Published in Journal of Business & Economic Statistics, Vol. 25, No. 3 (Jul., 2007), pp. 356-376, ht...
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
We propose a corrected plug-in method for constructing confidence intervals of the conditional quant...
Koenker & Basset, 1978 introduce the quantile regression estimator, that allows to have a more compl...
The Box-Cox quantile regression model using the two stage method introduced by Chamberlain (1994) an...
The Box-Cox quantile regression model using the two stage method introduced by Chamberlain (1994) an...
We present in this paper a few important direction on research using quantile regression. We start ...
The Box–Cox quantile regression model introduced by Powell (1991) is a flexible and numerically attr...
This paper presents an alternative approach to the likelihood methods for estimating the parameter A...
This paper presents an alternative approach to the likelihood methods for estimating the parameter A...
The Box-Cox method has been widely used to improve estimation accuracy in different fields, especial...
Powell (1986) proposed a quantile regression estimator for censored regression models on the basis o...
Modelling the quantiles of a random variable is facilitated by their equivariance to monotone transf...
A quantile-based method for estimating the Box-Cox transformation in random samples is presented. Th...
Transformation of a response variable can greatly expand the class of problems for which the linear ...
Published in Journal of Business & Economic Statistics, Vol. 25, No. 3 (Jul., 2007), pp. 356-376, ht...
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
We propose a corrected plug-in method for constructing confidence intervals of the conditional quant...
Koenker & Basset, 1978 introduce the quantile regression estimator, that allows to have a more compl...