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Title: Robust approaches to quantification of margin and uncertainty for sparse data

Abstract

Characterizing the tails of probability distributions plays a key role in quantification of margins and uncertainties (QMU), where the goal is characterization of low probability, high consequence events based on continuous measures of performance. When data are collected using physical experimentation, probability distributions are typically fit using statistical methods based on the collected data, and these parametric distributional assumptions are often used to extrapolate about the extreme tail behavior of the underlying probability distribution. In this project, we character- ize the risk associated with such tail extrapolation. Specifically, we conducted a scaling study to demonstrate the large magnitude of the risk; then, we developed new methods for communicat- ing risk associated with tail extrapolation from unvalidated statistical models; lastly, we proposed a Bayesian data-integration framework to mitigate tail extrapolation risk through integrating ad- ditional information. We conclude that decision-making using QMU is a complex process that cannot be achieved using statistical analyses alone.

Authors:
 [1];  [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1394061
Report Number(s):
SAND2017-9960
657010
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Hund, Lauren, Schroeder, Benjamin B., Rumsey, Kelin, and Murchison, Nicole. Robust approaches to quantification of margin and uncertainty for sparse data. United States: N. p., 2017. Web. doi:10.2172/1394061.
Hund, Lauren, Schroeder, Benjamin B., Rumsey, Kelin, & Murchison, Nicole. Robust approaches to quantification of margin and uncertainty for sparse data. United States. doi:10.2172/1394061.
Hund, Lauren, Schroeder, Benjamin B., Rumsey, Kelin, and Murchison, Nicole. Fri . "Robust approaches to quantification of margin and uncertainty for sparse data". United States. doi:10.2172/1394061. https://www.osti.gov/servlets/purl/1394061.
@article{osti_1394061,
title = {Robust approaches to quantification of margin and uncertainty for sparse data},
author = {Hund, Lauren and Schroeder, Benjamin B. and Rumsey, Kelin and Murchison, Nicole},
abstractNote = {Characterizing the tails of probability distributions plays a key role in quantification of margins and uncertainties (QMU), where the goal is characterization of low probability, high consequence events based on continuous measures of performance. When data are collected using physical experimentation, probability distributions are typically fit using statistical methods based on the collected data, and these parametric distributional assumptions are often used to extrapolate about the extreme tail behavior of the underlying probability distribution. In this project, we character- ize the risk associated with such tail extrapolation. Specifically, we conducted a scaling study to demonstrate the large magnitude of the risk; then, we developed new methods for communicat- ing risk associated with tail extrapolation from unvalidated statistical models; lastly, we proposed a Bayesian data-integration framework to mitigate tail extrapolation risk through integrating ad- ditional information. We conclude that decision-making using QMU is a complex process that cannot be achieved using statistical analyses alone.},
doi = {10.2172/1394061},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Sep 01 00:00:00 EDT 2017},
month = {Fri Sep 01 00:00:00 EDT 2017}
}

Technical Report:

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