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Arima model for forecasting Poisson ...

Fu, Wandong.

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Arima model for forecasting Poisson data: Application to long-term earthquake predictions.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Arima model for forecasting Poisson data: Application to long-term earthquake predictions./
作者:	Fu, Wandong.
面頁冊數:	53 p.
附註:	Source: Masters Abstracts International, Volume: 49-02, page: 1209.
Contained By:	Masters Abstracts International49-02.
標題:	Applied Mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1482291
ISBN:	9781124249032

Arima model for forecasting Poisson data: Application to long-term earthquake predictions.
Fu, Wandong.

Arima model for forecasting Poisson data: Application to long-term earthquake predictions. - 53 p.

Source: Masters Abstracts International, Volume: 49-02, page: 1209.

Thesis (M.S.)--University of Nevada, Las Vegas, 2010.

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.

ISBN: 9781124249032Subjects--Topical Terms:

1669109
Applied Mathematics.

Arima model for forecasting Poisson data: Application to long-term earthquake predictions.
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245 1 0 $a Arima model for forecasting Poisson data: Application to long-term earthquake predictions.
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500 $a Source: Masters Abstracts International, Volume: 49-02, page: 1209.
500 $a Adviser: Chih-Hsiang Ho.
502 $a Thesis (M.S.)--University of Nevada, Las Vegas, 2010.
520 $a 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.
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790 1 0 $a Ho, Chih-Hsiang, $e advisor
790 1 0 $a Amei, Amei $e committee member
790 1 0 $a Ghosh, Kaushik $e committee member
790 1 0 $a Chen, LeinLein $e committee member
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