The thesis investigates a pair of stationary stochastic process models whose domains
are the set of integers and the set of real numbers respectively. The stationary processes
with our specific correlation functions include the discrete and continuous first and second
order autoregressive processes as their special cases. The maximum likelihood method is
then applied to obtain the nonlinear equation system for the maximum likelihood estimators
of the model parameters and the solutions are found by using the deepest gradient algorithm.
The advantage of the algorithm lies in the calculation could be divided into several steps at
a cost of O(n) calculations per step. Finally, predictions are given for both simulated data
and Wichita temperature data. / Thesis (M.S.)--Wichita State University, College of Liberal Arts and Sciences, Dept. of Mathematics and Statistics / This research is supported in part by the Kansas NSF EPSCoR under Grant EPS
0903806 and in part by a Kansas Technology Enterprize Corporation grant on Understanding
Climate Change in the Great Plains: Source, Impact, and Mitigation.
| Identifer | oai:union.ndltd.org:WICHITA/oai:soar.wichita.edu:10057/3644 |
| Date | 08 1900 |
| Creators | Li, Qi |
| Contributors | Lu, Tianshi |
| Publisher | Wichita State University |
| Source Sets | Wichita State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | viii, 46 leaves, ill. |
| Rights | Copyright First Name Last Name, 2010. All rights reserved |
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