FUNCTIONAL ADDITIVE QUANTILE REGRESSION

Additive Models for Quantile Regression

We describe some recent development of nonparametric methods for estimating conditional quantile functions using additive models with total variation roughness penalties. We focus attention primarily on selection of smoothing parameters and on the con

Bayesian semiparametric additive quantile regression

Quantile regression provides a convenient framework for analyzing the impact of covariates on the complete conditional distribution of a response variable instead of only the mean. While frequentist treatments of quantile regression are typically completely nonparametric, a Bayesian formulation relies on assuming the asymmetric Laplace distribution as auxiliary error distribution that yields po...

www.econstor.eu Bayesian semiparametric additive quantile regression

Functional Additive Regression

We suggest a new method, called Functional Additive Regression, or FAR, for efficiently performing high dimensional functional regression. FAR extends the usual linear regression model involving a functional predictor, X(t), and a scalar response, Y , in two key respects. First, FAR uses a penalized least squares optimization approach to efficiently deal with high dimensional problems involving...

Additive Models for Quantile Regression: Some New Methods for R

This brief report describes some recent developments of the R quantreg package to incorporate methods for additive models. The methods are illustrated with an application to modeling childhood malnutrition in India. Models with additive nonparametric effects offer a valuable dimension reduction device throughout applied statistics. In this paper we describe some recent developments of additive ...