Quantile regression via vector generalized additive models
- 29 June 2004
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 23 (14), 2295-2315
- https://doi.org/10.1002/sim.1822
Abstract
One of the most popular methods for quantile regression is the LMS method of Cole and Green. The method naturally falls within a penalized likelihood framework, and consequently allows for considerable flexible because all three parameters may be modelled by cubic smoothing splines. The model is also very understandable: for a given value of the covariate, the LMS method applies a Box–Cox transformation to the response in order to transform it to standard normality; to obtain the quantiles, an inverse Box–Cox transformation is applied to the quantiles of the standard normal distribution. The purposes of this article are three-fold. Firstly, LMS quantile regression is presented within the framework of the class of vector generalized additive models. This confers a number of advantages such as a unifying theory and estimation process. Secondly, a new LMS method based on the Yeo–Johnson transformation is proposed, which has the advantage that the response is not restricted to be positive. Lastly, this paper describes a software implementation of three LMS quantile regression methods in the S language. This includes the LMS–Yeo–Johnson method, which is estimated efficiently by a new numerical integration scheme. The LMS–Yeo–Johnson method is illustrated by way of a large cross-sectional data set from a New Zealand working population. Copyright © 2004 John Wiley & Sons, Ltd.Keywords
This publication has 17 references indexed in Scilit:
- Quantile regression: applications and current research areasJournal of the Royal Statistical Society: Series D (The Statistician), 2003
- Reference curves based on non‐parametric quantile regressionStatistics in Medicine, 2002
- Quantile RegressionJournal of Economic Perspectives, 2001
- On an Alternative Solution to the Vector Spline ProblemJournal of the Royal Statistical Society Series B: Statistical Methodology, 1998
- A Comparison of Statistical Methods for Age-related Reference IntervalsJournal of the Royal Statistical Society Series A: Statistics in Society, 1997
- Estimating and Visualizing Conditional DensitiesJournal of Computational and Graphical Statistics, 1996
- R: A Language for Data Analysis and GraphicsJournal of Computational and Graphical Statistics, 1996
- Smoothing reference centile curves: The lms method and penalized likelihoodStatistics in Medicine, 1992
- Extreme value theory based on the r largest annual eventsJournal of Hydrology, 1986
- Symbolic Description of Factorial Models for Analysis of VarianceJournal of the Royal Statistical Society Series C: Applied Statistics, 1973