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Quantitative approach to technical p...

Lewis, Tiffany Lorraine.

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Quantitative approach to technical performance measurement and technical risk analysis utilizing Bayesian methods and Monte Carlo simulation.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Quantitative approach to technical performance measurement and technical risk analysis utilizing Bayesian methods and Monte Carlo simulation./
Author:	Lewis, Tiffany Lorraine.
Description:	290 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-04, Section: B, page: 2657.
Contained By:	Dissertation Abstracts International71-04B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3397235
ISBN:	9781109689464

Quantitative approach to technical performance measurement and technical risk analysis utilizing Bayesian methods and Monte Carlo simulation.
Lewis, Tiffany Lorraine.

Quantitative approach to technical performance measurement and technical risk analysis utilizing Bayesian methods and Monte Carlo simulation. - 290 p.

Source: Dissertation Abstracts International, Volume: 71-04, Section: B, page: 2657.

Thesis (Ph.D.)--The George Washington University, 2010.

Risk on a project or program is typically evaluated in terms of the triple constraint: scope, time, and cost. The Monte Carlo method (Metropolis & Ulam, 1949) is a widely accepted risk analysis technique and is deemed to be an effective way of analyzing the uncertainty associated with cost and schedule risks. More systems engineers and risk managers are taking advantage of this risk analysis technique to perform independent risk assessments on either cost or schedule; however, the analysis of technical risk using a statistical approach has not been formulated to a significant degree in the literature.

ISBN: 9781109689464Subjects--Topical Terms:

517247
Statistics.

Quantitative approach to technical performance measurement and technical risk analysis utilizing Bayesian methods and Monte Carlo simulation.
LDR:03103nam 2200361 4500 001 1391257
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008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781109689464
035 $a (UMI)AAI3397235
035 $a AAI3397235
040 $a UMI $c UMI
100 1 $a Lewis, Tiffany Lorraine. $3 1669661
245 1 0 $a Quantitative approach to technical performance measurement and technical risk analysis utilizing Bayesian methods and Monte Carlo simulation.
300 $a 290 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-04, Section: B, page: 2657.
500 $a Advisers: Thomas A. Mazzuchi; Shahram Sarkani.
502 $a Thesis (Ph.D.)--The George Washington University, 2010.
520 $a Risk on a project or program is typically evaluated in terms of the triple constraint: scope, time, and cost. The Monte Carlo method (Metropolis & Ulam, 1949) is a widely accepted risk analysis technique and is deemed to be an effective way of analyzing the uncertainty associated with cost and schedule risks. More systems engineers and risk managers are taking advantage of this risk analysis technique to perform independent risk assessments on either cost or schedule; however, the analysis of technical risk using a statistical approach has not been formulated to a significant degree in the literature.
520 $a This research study will explore the use of a statistical versus a deterministic approach to independently assess the risks associated with technical performance. The methodology discussed transforms a deterministic risk assessment model into a statistical model. Expert judgment is used within a Bayesian framework to develop baseline expected values for technical performance. Technical Performance Measurement (TPM), Monte Carlo simulation, and Risk software are used to calculate a system level Technical Risk Index Distribution (TRID). The TRID framework defines distribution ranges for each TPM and formulates risk index distributions. Actual data from a DoD TPM implementation project is used to validate the TRID framework. The proposed statistical model is verified on a hypothetical system where quantifiable TPMs have been identified that meet the criterion in the model proposed.
520 $a Keywords. Monte Carlo simulation, TPMs, technical performance measures, technical risk, technical performance risk, Monte Carlo risk analysis, sensitivity analysis, technical risk analysis, program management, risk management, project management, risk analysis, risk, quantitative risk analysis, expert judgment, Bayes Theorem, Bayesian methods, expert opinion.
590 $a School code: 0075.
650 4 $a Statistics. $3 517247
650 4 $a Engineering, System Science. $3 1018128
650 4 $a Operations Research. $3 626629
690 $a 0463
690 $a 0790
690 $a 0796
710 2 $a The George Washington University. $b Engineering Mgt and Systems Engineering. $3 1028210
773 0 $t Dissertation Abstracts International $g 71-04B.
790 1 0 $a Mazzuchi, Thomas A., $e advisor
790 1 0 $a Sarkani, Shahram, $e advisor
790 1 0 $a Murphree, Edward L. $e committee member
790 1 0 $a Allario, Frank $e committee member
790 1 0 $a Podolsky, Michael J. $e committee member
790 $a 0075
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3397235