Date of Award

Spring 2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational Analysis and Modeling

First Advisor

Bogdan Strimbu

Abstract

Statistical analysis is influenced by implementation of the algorithms used to execute the computations associated with various statistical techniques. Over many years; very important criteria for model comparison has been studied and examined, and two algorithms on a single dataset have been performed numerous times. The goal of this research is not comparing two or more models on one dataset, but comparing models with numerical algorithms that have been used to solve them on the same dataset.

In this research, different models have been broadly applied in modeling and their contrasting which are affected by the numerical algorithms in different SAS software procedures. Those model-algorithm combinations have been tested separately on three datasets: Box and Tiao Ozone data, simulated Tree Height-Age data, and Longleaf Pine Tree Diameter-Height (Taper) data.

Furthermore, results presented will be inclusive in describing the general conclusions by comparing the algorithms, then analyzing the behavior and performance of every algorithm based upon the verification and the results we have. In addition, algorithms' relative and absolute strengths and weaknesses will be identified. The decision will stand on well-known model selection criteria: Akaike Information Criterion (AIC), Schwarz's Bayesian Criterion (SBC), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2).

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