In quantile regression, various quantiles of a response variable Y are modelled as functions of covariates (rather than its mean). An important application is the construction of reference curves/surfaces and conditional prediction intervals for Y. Recently, a nonparametric quantile regression method based on the concept of optimal quantization was proposed. This method competes very well with k-nearest neighbor, kernel, and spline methods. In this paper, we describe an R package, called QuantifQuantile, that allows to perform quantization-based quantile regression. We describe the various functions of the package and provide examples.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
After its introduction by Koenker and Basset (1978), quantile regression has become an important and...
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
The CDF-quantile family of two-parameter distributions with support (0, 1) described in Smithson and...
In quantile regression, various quantiles of a response variable Y are modelled as func- tions of co...
Quantile regression allows to assess the impact of some covariate X on a response Y .An important ap...
Abstract. Quantile regression is an evolving body of statistical methods for estimating and drawing ...
Modeling quantile regression coefficients functions permits describing the coefficients of a quanti...
BSquare in an R package to conduct Bayesian quantile regression for continuous, discrete, and censor...
The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and m...
summary:Recently, the eminently popular standard quantile regression has been generalized to the mul...
A guide to the implementation and interpretation of Quantile Regression models. This book explores t...
[an updated version is now online here] After my series of post on classification algorithms, it's t...
National audienceLa régression quantile permet d'évaluer l'impact de covariables X sur une variable ...
Quantile regression extends the statistical analysis of the response models beyond conditional means...
Inference in quantile analysis has received considerable attention in the recent years. Linear quant...
After its introduction by Koenker and Basset (1978), quantile regression has become an important and...
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
The CDF-quantile family of two-parameter distributions with support (0, 1) described in Smithson and...
In quantile regression, various quantiles of a response variable Y are modelled as func- tions of co...
Quantile regression allows to assess the impact of some covariate X on a response Y .An important ap...
Abstract. Quantile regression is an evolving body of statistical methods for estimating and drawing ...
Modeling quantile regression coefficients functions permits describing the coefficients of a quanti...
BSquare in an R package to conduct Bayesian quantile regression for continuous, discrete, and censor...
The R package quantreg.nonpar implements nonparametric quantile regression methods to estimate and m...
summary:Recently, the eminently popular standard quantile regression has been generalized to the mul...
A guide to the implementation and interpretation of Quantile Regression models. This book explores t...
[an updated version is now online here] After my series of post on classification algorithms, it's t...
National audienceLa régression quantile permet d'évaluer l'impact de covariables X sur une variable ...
Quantile regression extends the statistical analysis of the response models beyond conditional means...
Inference in quantile analysis has received considerable attention in the recent years. Linear quant...
After its introduction by Koenker and Basset (1978), quantile regression has become an important and...
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
The CDF-quantile family of two-parameter distributions with support (0, 1) described in Smithson and...