Date of Award
8-2014
Document Type
Thesis
Degree Name
Master of Science (MS)
Legacy Department
Mathematical Science
Committee Chair/Advisor
GALLAGHER, COLLIN M
Committee Member
MCMAHAN , CHRISTOPHER S
Committee Member
LUND , ROBERT B
Abstract
Quantile regression is a developing statistical tool which is used to explain the relationship between response and predictor variables. This thesis describes two examples of climatology using quantile regression.Our main goal is to estimate derivatives of a conditional mean and/or conditional quantile function. We introduce a method to handle autocorrelation in the framework of quantile regression and used it with the temperature data. Also we explain some properties of the tornado data which is non-normally distributed. Even though quantile regression provides a more comprehensive view, when talking about residuals with the normality and the constant variance assumption, we would prefer least square regression for our temperature analysis. When dealing with the non-normality and non constant variance assumption, quantile regression is a better candidate for the estimation of the derivative.
Recommended Citation
Marasinghe, Dilhani, "QUANTILE REGRESSION FOR CLIMATE DATA" (2014). All Theses. 1909.
https://tigerprints.clemson.edu/all_theses/1909