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March 2014

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*_Notice and Invitation_*
Oral Defense of Doctoral Dissertation
The Volgenau School of Engineering, George Mason University


*Michael Wright*
Bachelor of Arts, University of Virginia, 2008
Master of Science, George Mason University, 2011

  

*Evaluation of heterogeneity statistics for regional frequency analysis*

Monday, April 28^th , 2014, 1:00 PM to 3:00 PM
The Nguyen Engineering Building, Room 1602

All are invited to attend.

*_Committee_*
Dr. Mark Houck, Chair
Dr. Celso Ferreira, Co-Chair
Dr. Shanjiang Zhu

Dr. Stephen Nash

Dr. Jason Giovannettone

*_Abstract_*

Precipitation gauge records are often pooled to reduce the error of 
quantile estimates by increasing sample size. This introduces a new 
error component proportional to heterogeneity, the degree to which the 
constituent gauges diverge from the regional average. Precipitation 
analysts, especially those working in data-sparse regions of the world, 
often use heterogeneity statistics to determine whether a candidate 
regionalization of gauges would increase or decrease the error of 
quantile estimates. This dissertation assesses the relationship between 
error in quantile estimates and five heterogeneity statistics proposed 
in the literature. Analysts can use this research to defend the choice 
of certain heterogeneity statistics over others and to establish 
thresholds below which candidate regionalizations can be declared 
acceptably homogeneous. All five-or-more-site regionalizations of a 
twelve-gauge Minnesota dataset are enumerated and Monte Carlo simulation 
is used to estimate quantile error and the heterogeneity statistics. 
Linear relationships found between heterogeneity estimators and quantile 
error across these real data are compared to relationships found in 
simulation experiments isolating the heterogeneity-related component of 
quantile error. Two statistics have highly linear relationships to error 
in both the simulation and enumeration studies. The less linear 
statistic is more robust to deviation from the hypothesis that regional 
coefficient of variation and skewness ranges increase in tandem as 
heterogeneity rises. Novel heterogeneity thresholds are defined for this 
statistic.

A copy of this doctoral dissertation is on reserve at the Johnson Center 
Library.



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