Abstract:The development and dissemination of quantile regression (QR) started with the formulation of the QR problem as a linear programming problem. Such formulation allows to exploit efficient methods and a...The development and dissemination of quantile regression (QR) started with the formulation of the QR problem as a linear programming problem. Such formulation allows to exploit efficient methods and algorithms to solve a complex optimization problem offering the way to explore the whole conditional distribution of a variable and not only its center. After an introduction to the linear programming approach for solving the QR problem, this chapter focuses on the added value of QR exploring its features in the case of regression models with homogeneous, heterogeneous and dependent error models. Subsequently, a set of artificial data is used to show several QR features. A section focuses on the interpretation of the QR estimated coefficients by drawing a parallel between homogeneous and heterogeneous regression models.Read More
Publication Year: 2014
Publication Date: 2014-08-04
Language: en
Type: other
Indexed In: ['crossref']
Access and Citation
Cited By Count: 1
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot