EconPapers    
Economics at your fingertips  
 

Parametric modeling of quantile regression coefficient functions

Paolo Frumento and Matteo Bottai

Biometrics, 2016, vol. 72, issue 1, 74-84

Abstract: type="main" xml:lang="en">

Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile regression is foremost among the utilized methods. The coefficients of a quantile regression model depend on the order of the quantile being estimated. For example, the coefficients for the median are generally different from those of the 10th centile. In this article, we describe an approach to modeling the regression coefficients as parametric functions of the order of the quantile. This approach may have advantages in terms of parsimony, efficiency, and may expand the potential of statistical modeling. Goodness-of-fit measures and testing procedures are discussed, and the results of a simulation study are presented. We apply the method to analyze the data that motivated this work. The described method is implemented in the qrcm R package.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
http://hdl.handle.net/ (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:72:y:2016:i:1:p:74-84

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0006-341X

Access Statistics for this article

More articles in Biometrics from The International Biometric Society
Bibliographic data for series maintained by Wiley Content Delivery (contentdelivery@wiley.com).

 
Page updated 2024-09-05
Handle: RePEc:bla:biomet:v:72:y:2016:i:1:p:74-84