A Gentle Introduction to Quantile Regression for Ecologists

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 no...

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Bibliographic Details
Published in:Frontiers in ecology and the environment 2003-10, Vol.1 (8), p.412-420
Main Authors: Cade, Brian S., Noon, Barry R.
Format: Article
Language:English
Subjects:
Confidence interval
Ecological modeling
Human ecology
Least squares
Linear regression
Quantile regression
Quantiles
Rates of change
Regression analysis
Reviews
Statistical discrepancies
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Summary: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.
ISSN:1540-9295
1540-9309
DOI:10.1890/1540-9295(2003)001[0412:AGITQR]2.0.CO;2