Date of Award
1-1-2010
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
College/School/Department
Department of Epidemiology and Biostatistics
Program
Biostatistics
Content Description
1 online resource (xix, 201 pages) : illustrations (some color), color maps.
Dissertation/Thesis Chair
Igor G Zurbenko
Committee Members
Howard H Stratton, Recai Yucel, Steven Samuels, Robert F Henry
Keywords
B-spline, El Nino, Global warming, KZS, Smoothing, Time series, Smoothing (Statistics), Climatic changes, Spline theory
Subject Categories
Climate | Statistics and Probability
Abstract
In statistics, smoothing is a technique that attempts to capture the key patterns or trends in data while leaving out the noise that is obscuring them. Nonparametric techniques are well-suited for smoothing as they do not rely on assumptions that the data arise from a given probability distribution.
Recommended Citation
Cyr, Derek Daniel, "A spline kernel based smoothing algorithm : a comparison of methods with a spatiotemporal application to global climate fluctuations" (2010). Legacy Theses & Dissertations (2009 - 2024). 164.
https://scholarsarchive.library.albany.edu/legacy-etd/164