Student Seminar Series - June 13, 2006
University of Minnesota
School of Statistics
College of Liberal Arts
A
Review of Quantile Regression with Application to Low Birth Weight Data
Marit
Gilkey
Tuesday, June 13, 2006
1:00 PM, B29
Ford Hall
Minneapolis, East Bank Campus
Refreshments at 12:30 PM
300 Ford Hall
Abstract
For decades, ordinary least squares regression has been used to
investigate
the relationship between a response and the covariate(s). Quantile
regression has not been around as long, but can give a more complete
picture
of the relationship between variables by modeling the conditional
distribution of the response given the covariates at different
quantiles. This project provides a short review of the theory and
applications of
quantile regression, including estimation of conditional quantiles,
hypothesis testing, and model selection. Simulation results are
provided to
compare quantile regression with least squares regression. The
advantage of
quantile regression is observed in several situations. To illustrate
the
application, the quantile regression method is used to analyze a low
birth
weight data set.