Tolerance limits under normal mixtures: Application to the evaluation of nuclear power plant safety and to the assessment of circular error probable

Zachary Zimmer, DoHwan Park and Thomas Mathew

Computational Statistics & Data Analysis, 2016, vol. 103, issue C, 304-315

Abstract: Upper tolerance limits are derived for (i) a normal mixture distribution, and (ii) for the distribution of the Euclidean norm of a bivariate normal mixture random variable, using asymptotic normality of the MLE and bootstrap calibration. Scenario (i) is used to model the peak cladding temperature (PCT) of nuclear power plants, to assess if the PCT distribution is below a safety threshold of 2200 °F. Scenario (ii) is used to model impact location data on projectiles launched from different locations or systems, and the problem is inference concerning the circular error probable (CEP). Simulation studies show the accuracy of the proposed methodology.

Keywords: Bootstrap calibration; Delta method; Impact accuracy; Loss of coolant accident (LOCA); Peak cladding temperature (PCT) (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947316301177
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:103:y:2016:i:c:p:304-315

DOI: 10.1016/j.csda.2016.05.015

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().