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A gentle introduction to quantile regression for ecologists

January 1, 2003

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.

Citation Information

Publication Year 2003
Title A gentle introduction to quantile regression for ecologists
DOI 10.1890/1540-9295(2003)001[0412:AGITQR]2.0.CO;2
Authors B.S. Cade, B.R. Noon
Publication Type Article
Publication Subtype Journal Article
Series Title Frontiers in Ecology and the Environment
Series Number
Index ID 1015331
Record Source USGS Publications Warehouse
USGS Organization Fort Collins Science Center