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A Dynamic System Using Baseline Weib...

Cao, Quy Xuan.

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A Dynamic System Using Baseline Weibull Distribution Based on Sequential Order Statistics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Dynamic System Using Baseline Weibull Distribution Based on Sequential Order Statistics./
作者:	Cao, Quy Xuan.
面頁冊數:	46 p.
附註:	Source: Masters Abstracts International, Volume: 54-06.
Contained By:	Masters Abstracts International54-06(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1594486
ISBN:	9781321917710

A Dynamic System Using Baseline Weibull Distribution Based on Sequential Order Statistics.
Cao, Quy Xuan.

A Dynamic System Using Baseline Weibull Distribution Based on Sequential Order Statistics. - 46 p.

Source: Masters Abstracts International, Volume: 54-06.

Thesis (M.S.)--University of South Dakota, 2015.

In this research, the Weibull distribution is proposed for the baseline model of lifetimes of components in a composite system. In this composite system, the failure of a component induces a higher load on the surviving components and increases component hazard rate via a power-trend process. The likelihood function based on the component lifetimes of the composite system is studied, and the likelihood equations and maximum likelihood estimators for model parameters are established. Intensive simulations have been conducted to evaluate the performance of the proposed procedures.

ISBN: 9781321917710Subjects--Topical Terms:

517247
Statistics.

A Dynamic System Using Baseline Weibull Distribution Based on Sequential Order Statistics.
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500 $a Adviser: Yuhlong Lio.
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520 $a In this research, the Weibull distribution is proposed for the baseline model of lifetimes of components in a composite system. In this composite system, the failure of a component induces a higher load on the surviving components and increases component hazard rate via a power-trend process. The likelihood function based on the component lifetimes of the composite system is studied, and the likelihood equations and maximum likelihood estimators for model parameters are established. Intensive simulations have been conducted to evaluate the performance of the proposed procedures.
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