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A gentle introduction to quantile regression for ecologists

Frontiers in Ecology and the Environment

By:
and
https://doi.org/10.1890/1540-9295(2003)001[0412:AGITQR]2.0.CO;2

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Abstract

Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.

Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
A gentle introduction to quantile regression for ecologists
Series title:
Frontiers in Ecology and the Environment
DOI:
10.1890/1540-9295(2003)001[0412:AGITQR]2.0.CO;2
Volume:
1
Issue:
8
Year Published:
2003
Language:
English
Publisher:
Wiley
Contributing office(s):
Fort Collins Science Center
Description:
9 p.
First page:
412
Last page:
420