Award Date

8-2010

Degree Type

Thesis

Degree Name

Master of Science in Mathematical Science

Department

Mathematical Sciences

First Committee Member

Chih-Hsiang Ho, Chair

Second Committee Member

Amei Amei

Third Committee Member

Kaushik Ghosh

Graduate Faculty Representative

LeinLein Chen

Number of Pages

53

Abstract

Earthquakes that occurred worldwide during the period of 1896 to 2009 with magnitude greater than or equal to 8.0 on the Richter scale are assumed to follow a Poisson process. Autoregressive Integrated Moving Average models are presented to fit the empirical recurrence rates, and to predict future large earthquakes. We show valuable modeling and computational techniques for the point processes and time series data. Specifically, for the proposed methodology, we address the following areas: data management and graphic presentation, model fitting and selection, model validation, model and data sensitivity analysis, and forecasting.

Keywords

Autoregressive Integrated Moving Average (ARIMA); Earthquake prediction; Poisson processes

Disciplines

Applied Statistics | Geophysics and Seismology | Mathematics | Statistics and Probability

Language

English


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